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568 results on '"Zaniolo, Carlo"'

51. Fast and Light Boosting for Adaptive Mining of Data Streams

Author

Chu, Fang, Zaniolo, Carlo, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Dai, Honghua, editor, Srikant, Ramakrishnan, editor, and Zhang, Chengqi, editor

Published
2004
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54. Efficient Complex Query Support for Multiversion XML Documents

Author

Chien, Shu-Yao, Tsotras, Vassilis J., Zaniolo, Carlo, Zhang, Donghui, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Jensen, Christian S., editor, Šaltenis, Simonas, editor, Jeffery, Keith G., editor, Pokorny, Jaroslav, editor, Bertino, Elisa, editor, Böhn, Klemens, editor, and Jarke, Matthias, editor

Published
2002
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56. Version Management of XML Documents

Author

Shu-Yao, Chien, Tsotras, Vassilis J., Zaniolo, Carlo, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Suciu, Dan, editor, and Vossen, Gottfried, editor

Published
2001
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64. Logic-Based User-Defined Aggregates for the Next Generation of Database Systems

Author

Zaniolo, Carlo, Wang, Haixun, Amarel, S., editor, Biermann, A., editor, Bolc, L., editor, Hayes, P., editor, Joshi, A., editor, Lenat, D., editor, Loveland, D. W., editor, Mackworth, A., editor, Nau, D., editor, Reiter, R., editor, Sandewall, E., editor, Shafer, S., editor, Shoham, Y., editor, Siekmann, J., editor, Wahlster, W., editor, Apt, Krzysztof R., editor, Marek, Victor W., editor, Truszczynski, Mirek, editor, and Warren, David S., editor

Published
1999
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65. Optimization of logic queries with MIN and MAX predicates

Author

Greco, Sergio, Zaniolo, Carlo, Ganguly, Sumit, Carbonell, Jaime G., editor, Seikmann, Jörg, editor, Goos, G., editor, Hartmanis, J., editor, van Leeuwen, J., editor, Andreasen, Troels, editor, Christiansen, Henning, editor, and Larsen, Henrik Legind, editor

Published
1998
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79. Aves: A Decision Engine for Energy-efficient Stream Analytics across Low-power Devices

Author

Das, Roshan Bharath, Makkes, Marc X., Uta, Alexandru, Wang, Lin, Bal, Henri, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny, Computer Systems, Network Institute, High Performance Distributed Computing, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, and Ye, Yanfang Fanny

Subjects
Decision support system, Computer science, Real-time computing, Wearable computer, 020206 networking & telecommunications, 02 engineering and technology, Simplicity (photography), Factor (programming language), 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Power semiconductor device, SDG 7 - Affordable and Clean Energy, computer, Energy (signal processing), computer.programming_language, Efficient energy use
Abstract

Today's low-power devices, such as smartphones and wearables, form a very heterogeneous ecosystem. Applications in such a system typically follow a reactive pattern based on stream analytics, i.e., sensing, processing, and actuating. Despite the simplicity of this pattern, deciding where to place the processing tasks of an application to achieve energy efficiency is non-trivial in a heterogeneous system since application components are distributed across multiple devices. In this paper, we present Aves - a decision-making engine based on a holistic energy-prediction model, with which the processing tasks of applications can be placed automatically in an energy-efficient manner without programmer/user intervention. We validate the effectiveness of the model and reveal several counter-intuitive placement decisions. Our decision engine's improvements are typically 10-30%, with up to a factor 14 in the most extreme cases. We also show that Aves gives an accurate decision in comparison with real energy measurements for two sensor-based applications.

Published
2020
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81. Non-determinism in deductive databases

Author

Giannotti, Fosca, Pedreschi, Dino, Saccà, Domenico, Zaniolo, Carlo, Goods, Gerhard, editor, Hartmanis, Juris., editor, Delobel, C., editor, Kifer, M., editor, and Masunaga, Y., editor

Published
1991
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82. Deductive Databases-theory meets practice

Author

Zaniolo, Carlo, Goos, G., editor, Hartmanis, J., editor, Barstow, D., editor, Brauer, W., editor, Brinch Hansen, P., editor, Gries, D., editor, Luckham, D., editor, Moler, C., editor, Pnueli, A., editor, Seegmüller, G., editor, Stoer, J., editor, Wirth, N., editor, Bancilhon, François, editor, Thanos, Constantino, editor, and Tsichritzis, Dennis, editor

Published
1990
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85. Batch Mode Active Learning for Individual Treatment Effect Estimation

Author

Puha, Zoltan, Kaptein, Maurits, Lemmens, Aurelie, Di Fatta, Giuseppe, Sheng, Victor, Cuzzocrea, Alfredo, Zaniolo, Carlo, Wu, Xindong, and Department of Marketing Management

Subjects
Active learning (machine learning), business.industry, Computer science, Bayesian probability, Sample (statistics), 02 engineering and technology, Maximization, Machine learning, computer.software_genre, 01 natural sciences, Regression, Field (computer science), 010104 statistics & probability, 0202 electrical engineering, electronic engineering, information engineering, Batch processing, 020201 artificial intelligence & image processing, Artificial intelligence, 0101 mathematics, business, computer
Abstract

Field experimentation has become a well-established practice to estimate individual treatment effects. In recent years, the Active Learning (AL) literature has developed methods to optimize the design of field experiments and reduce their cost. In this paper, we propose a novel AL algorithm for individual treatment effect estimation that works in batch mode for cases where the outcomes of an intervention are not immediate. It uniquely combines Expected Model Change Maximization and Bayesian Additive Regression Trees. Our approach (B-EMCMITE) uses the predictive uncertainty around the individual treatment effects to actively sample new units for experimentation and decide which treatment they will receive. We perform extensive simulations and test our approach on semi-synthetic, real-life data. B-EMCMITE outperforms alternative approaches and substantially reduces the number of observations needed to estimate individual treatment effects compared to A/B tests.

Published
2021

87. Windows

Author

Zaniolo, Carlo, LIU, LING, editor, and ÖZSU, M. TAMER, editor

Published
2009
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88. Hide and Mine in Strings: Hardness and Algorithms

Author

Bernardini, Giulia, Conte, Alessio, Gourdel, Garance, Grossi, Roberto, Loukides, Grigorios, Pisanti, Nadia, Pissis, Solon P., Punzi, Giulia, Stougie, Leen, Sweering, Michelle, Plant, Claudia, Wang, Haixun, Cuzzocrea, Alfredo, Zaniolo, Carlo, Wu, Xindong, Claudia Plant Haixun Wang Alfredo Cuzzocrea Carlo Zaniolo Xindong Wu, Bernardini, Giulia, Conte, Alessio, Gourdel, Garance, Grossi, Roberto, Loukides, Grigorio, Pisanti, Nadia, Pissis, Solon P., Punzi, Giulia, Stougie, Leen, Sweering, Michelle, Centrum Wiskunde & Informatica, Amsterdam (CWI), The Netherlands, Claudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu, Bernardini, G, Conte, A, Gourdel, G, Grossi, R, Loukides, G, Pisanti, N, Pissis, S, Punzi, G, Stougie, L, Sweering, M, Plant, Claudia, Wang, Haixun, Cuzzocrea, Alfredo, Zaniolo, Carlo, Wu, Xindong, Bioinformatics, AIMMS, Bio Informatics (IBIVU), Operations Analytics, Tinbergen Institute, Amsterdam Business Research Institute, Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), University of Pisa - Università di Pisa, Scalable, Optimized and Parallel Algorithms for Genomics (GenScale), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-GESTION DES DONNÉES ET DE LA CONNAISSANCE (IRISA-D7), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Département d'informatique - ENS Paris (DI-ENS), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de Recherche en Informatique et en Automatique (Inria)-Centre National de la Recherche Scientifique (CNRS), Equipe de recherche européenne en algorithmique et biologie formelle et expérimentale (ERABLE), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), King‘s College London, Centrum Wiskunde & Informatica (CWI), Vrije Universiteit Amsterdam [Amsterdam] (VU), and Département d'informatique de l'École normale supérieure (DI-ENS)

Subjects
String algorithms, Relation (database), Computer science, Context (language use), 02 engineering and technology, [INFO] Computer Science [cs], Data privacy, data sanitization, knowledge hiding, frequent pattern mining, string algorithms, Knowledge hiding, 020204 information systems, Frequent pattern mining, 0202 electrical engineering, electronic engineering, information engineering, Data sanitization, [INFO]Computer Science [cs], Spurious relationship, Time complexity, Integer programming, Computer Science::Cryptography and Security, String (computer science), Approximation algorithm, data sanitization, string algorithms, knowledge hiding, Data privacy, frequent pattern mining, 020201 artificial intelligence & image processing, Computational problem, Algorithm
Abstract

International audience; We initiate a study on the fundamental relationbetween data sanitization (i.e., the process of hiding confidentialinformation in a given dataset) and frequent pattern mining, inthe context of sequential (string) data. Current methods for stringsanitization hide confidential patterns introducing, however, anumber of spurious patterns that may harm the utility offrequent pattern mining. The main computational problem isto minimize this harm. Our contribution here is twofold. First,we present several hardness results, for different variants of thisproblem, essentially showing that these variants cannot be solvedor even be approximated in polynomial time. Second, we proposeinteger linear programming formulations for these variants andalgorithms to solve them, which work in polynomial time undercertain realistic assumptions on the problem parameters.

Published
2020
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89. A Programming Framework for Heterogeneous Stream Analytics

Author

Das, Roshan Bharath, Makkes, Marc X., Uta, Alexandru, Wang, Lin, Bal, Henri, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, Ye, Yanfang Fanny, Computer Systems, Network Institute, High Performance Distributed Computing, Baru, Chaitanya, Huan, Jun, Khan, Latifur, Hu, Xiaohua Tony, Ak, Ronay, Tian, Yuanyuan, Barga, Roger, Zaniolo, Carlo, Lee, Kisung, and Ye, Yanfang Fanny

Subjects
SDG 16 - Peace, Computer science, business.industry, SDG 16 - Peace, Justice and Strong Institutions, Big data, 020207 software engineering, Cloud computing, 02 engineering and technology, computer.software_genre, Justice and Strong Institutions, Software framework, Human–computer interaction, 020204 information systems, 0202 electrical engineering, electronic engineering, information engineering, Use case, Cloudlet, business, computer
Abstract

Sensor-based applications using Big Data are of increasing importance in various fields. A typical example of such use cases is building health-care applications [1], [2]. A typical scenario is where a patient's heart rate is monitored by a smartwatch. A smartphone can then analyze the gathered data and identify patterns in the patient's heart rate. However, if the data analysis is too complex to be performed on a smartphone, the computation could be offloaded to a nearby cloudlet or a remote cloud. A decision usually follows the analysis, and actuation is performed accordingly (e.g., a message is sent to either the patient or the doctor). Developing such an application is intrinsically complex, as the programmer needs to reconcile different APIs specific to different platforms.

Published
2019
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98. Temporal Information Management Using XML

Author

Wang, Fusheng, Zhou, Xin, Zaniolo, Carlo, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Atzeni, Paolo, editor, Chu, Wesley, editor, Lu, Hongjun, editor, Zhou, Shuigeng, editor, and Ling, Tok-Wang, editor

Published
2004
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100. Expressing and optimizing sequence queries in database systems

Author

Sadri, Reza, Zaniolo, Carlo, Zarkesh, Amir, and Adibi, Jafar

Subjects
Database, CD-ROM database, CD-ROM catalog, Databases -- Research
Abstract

The need to search for complex and recurring patterns in database sequences is shared by many applications. In this paper, we investigate the design and optimization of a query language capable of expressing and supporting efficiently the search for complex sequential patterns in database systems. Thus, we first introduce SQL-TS, an extension of SQL to express these patterns, and then we study how to optimize the queries for this language. We take the optimal text search algorithm of Knuth, Morris and Pratt, and generalize it to handle complex queries on sequences. Our algorithm exploits the interdependencies between the elements of a pattern to minimize repeated passes over the same data. Experimental results on typical sequence queries, such as double bottom queries, confirm that substantial speedups are achieved by our new optimization techniques. Categories and Subject Descriptors: H.2.3 [Database Management]: Languages--query languages; H.2.4 [Database Management]: Systems--query processing General Terms: Algorithms, Theory, Languages Additional Key Words and Phrases: Time series, sequences, query optimization, searching

Published
2004

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