@article{journals/ijwin/Cavalieri07, title = {WLAN-based Outdoor Localisation Using Pattern Matching Algorithm.}, author = {Salvatore Cavalieri}, journal = {IJWIN}, number = {4}, pages = {265-279}, url = {http://dblp.uni-trier.de/db/journals/ijwin/ijwin14.html#Cavalieri07}, volume = {14}, year = {2007}, biburl = {http://www.bibsonomy.org/bibtex/2af2bb21db6fa6361fd39675471f2f959/dblp}, description = {dblp}, ee = {http://dx.doi.org/10.1007/s10776-007-0067-0}, date = {2008-07-23}, keywords = {dblp } } @article{tkde06, title = {Fast and Memory Efficient Mining of Frequent Closed Itemsets}, author = {Claudio Lucchese and Salvatore Orlando and Raffaele Perego}, journal = {IEEE Transactions On Knowledge and Data Engineering}, number = {1}, pages = {21--36}, volume = {18}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/23aff1098bf9828a0c6683f07145d60bb/claudiolucchese}, keywords = {journal } } @article{is08, title = {A Constraint-based Querying System for Exploratory Pattern Discovery}, author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}, journal = {Infomation Systems}, pages = {(To Appear)}, publisher = {Elsevier}, year = {2008}, biburl = {http://www.bibsonomy.org/bibtex/2b12c337679601f651d02deebfee8f449/claudiolucchese}, keywords = {journal } } @inproceedings{orlando02efficient, title = {An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets}, author = {Salvatore Orlando and Paolo Palmerini and Raffaele Perego and Fabrizio Silvestri}, booktitle = {High Performance Computing for Computational Science — VECPAR 2002}, pages = {3--29}, url = {http://dx.doi.org/10.1007/3-540-36569-9_28}, year = {2003}, biburl = {http://www.bibsonomy.org/bibtex/2522c68b8bb5e28f1bf9f1e11e612f542/jaeschke}, description = {SpringerLink - Book Chapter}, abstract = {Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.}, keywords = {algorithm fca frequent itemset mining parallel set } } @inproceedings{orlando02efficient, title = {An Efficient Parallel and Distributed Algorithm for Counting Frequent Sets}, author = {Salvatore Orlando and Paolo Palmerini and Raffaele Perego and Fabrizio Silvestri}, booktitle = {High Performance Computing for Computational Science — VECPAR 2002}, pages = {3--29}, url = {http://dx.doi.org/10.1007/3-540-36569-9_28}, year = {2003}, biburl = {http://www.bibsonomy.org/bibtex/2522c68b8bb5e28f1bf9f1e11e612f542/hpclabisti}, description = {SpringerLink - Book Chapter}, abstract = {Due to the huge increase in the number and dimension of available databases, efficient solutions for counting frequent sets are nowadays very important within the Data Mining community. Several sequential and parallel algorithms were proposed, whichin many cases exhibit excellent scalability. In this paper we present ParDCI, a distributed and multithreaded algorithm forcounting the occurrences of frequent sets within transactional databases. ParDCI is a parallel version of DCI (Direct Count& Intersect), a multi-strategy algorithm which is able to adapt its behavior not only to the features of the specific computingplatform (e.g. available memory), but also to the features of the dataset being processed (e.g. sparse or dense datasets).ParDCI enhances previous proposals by exploiting the highly optimized counting and intersection techniques of DCI, and byrelying on a multi-level parallelization approachwh ichex plicitly targets clusters of SMPs, an emerging computing platform.We focused our work on the efficient exploitation of the underlying architecture. Intra-Node multithreading effectively exploitsthe memory hierarchies of each SMP node, while Inter-Node parallelism exploits smart partitioning techniques aimed at reducingcommunication overheads. In depth experimental evaluations demonstrate that ParDCI reaches nearly optimal performances undera variety of conditions.}, keywords = {data-mining } } @inproceedings{silvestri03hybrid, title = {A Hybrid Strategy for Caching Web Search Engine Results}, author = {Fabrizio Silvestri and Tiziano Fagni and Salvatore Orlando and Paolo Palmerini and Raffaele Perego}, booktitle = {WWW2003}, url = {http://bibserv.isti.cnr.it/Dienst/UI/2.0/Describe/cnr.isti/2003-A2-91?tiposearch=ercim\&\#38;langver=}, year = {2003}, biburl = {http://www.bibsonomy.org/bibtex/2857796949f71f3a5438a2aba74c930c1/hpclabisti}, posted-at = {2005-11-15 17:11:50}, priority = {2}, citeulike-article-id = {394243}, keywords = {ir } } @inproceedings{silvestri04searcharchitecture, title = {A Search Architecture for Grid Software Components}, address = {Los Alamitos, CA, USA}, author = {Fabrizio Silvestri and Diego Puppin and Domenico Laforenza and Salvatore Orlando}, booktitle = {WI '04: Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence}, pages = {495-498}, publisher = {IEEE Computer Society}, volume = {0}, year = {2004}, biburl = {http://www.bibsonomy.org/bibtex/2c1719e70925ce37296bf2406770b549b/hpclabisti}, description = {A Search Architecture for Grid Software Components}, isbn = {0-7695-2100-2}, doi = {http://doi.ieeecomputersociety.org/10.1109/WI.2004.10026}, keywords = {grid } } @inproceedings{silvestri04wings, title = {WINGS: A Parallel Indexer for Web Contents}, author = {Fabrizio Silvestri and Salvatore Orlando and Raffaele Perego}, booktitle = {Computational Science - ICCS 2004}, pages = {263--270}, year = {2004}, biburl = {http://www.bibsonomy.org/bibtex/217be2eddbca695bab71baffb599edfff/hpclabisti}, keywords = {ir } } @article{silvestri06toward, title = {Toward a search architecture for software components}, address = {HPC-Lab, ISTI-CNR, Via G. Moruzzi 1, 56124 Pisa, Italy; CS Department, University of Venice, Via Torino 155, 30172 Mestre, Italy}, author = {Fabrizio Silvestri and Diego Puppin and Domenico Laforenza and Salvatore Orlando}, journal = {Concurrency and Computation: Practice and Experience}, number = {10}, pages = {1317-1331}, publisher = {Copyright © 2005 John Wiley & Sons, Ltd.}, url = {http://dx.doi.org/10.1002/cpe.995}, volume = {18}, year = {2006}, biburl = {http://www.bibsonomy.org/bibtex/257ae69670275c5b131eeb1b698d189f0/hpclabisti}, description = {Wiley InterScience :: JOURNALS :: Concurrency and Computation: Practice and Experience}, doi = {10.1002/cpe.995}, keywords = {grid } } @inproceedings{silvestri04assigning, title = {Assigning document identifiers to enhance compressibility of Web Search Engines indexes}, address = {New York, NY, USA}, author = {Fabrizio Silvestri and Raffaele Perego and Salvatore Orlando}, booktitle = {SAC '04: Proceedings of the 2004 ACM symposium on Applied computing}, pages = {600--605}, publisher = {ACM}, url = {http://portal.acm.org/citation.cfm?id=967900.968024&coll=ACM&dl=ACM&CFID=36690833&CFTOKEN=36487312}, year = {2004}, biburl = {http://www.bibsonomy.org/bibtex/21abfa02170194d95f5e3b84d0771b22f/hpclabisti}, description = {Assigning document identifiers to enhance compressibility of Web Search Engines indexes}, abstract = {Granting efficient accesses to the index is a key issue for the performances of Web Search Engines (WSE). In order to enhance memory utilization and favor fast query resolution, WSEs use Inverted File (IF) indexes where the posting lists are stored as sequences of d_gaps (i.e. differences among successive document identifiers) compressed using variable length encoding methods. This paper describes the use of a lightweight clustering algorithm aimed at assigning the identifiers to documents in a way that minimizes the average values of d_gaps. The simulations performed on a real dataset, i.e. the Google contest collection, show that our approach allows to obtain an IF index which is, depending on the d_gap encoding chosen, up to 23% smaller than the one built over randomly assigned document identifiers. Moreover, we will show, both analytically and empirically, that the complexity of our algorithm is linear in space and time.}, location = {Nicosia, Cyprus}, isbn = {1-58113-812-1}, doi = {http://doi.acm.org/10.1145/967900.968024}, keywords = {data-mining ir } }