LEADER 00700nac# 22002053i 450 001 VAN00116696 005 20240806100757.364 100 $a20180619f2013 |0itac50 ba 102 $aIT 105 $a|||| ||||| 110 $ab|||||||||| 200 1 $aAbaco 210 $aRoma$cGB Editoria. 463 1$1001VAN00116694$12001 $aCasistiche e contraddizioni nel restauro architettonico$fPaolo Fancelli$1205 $aRoma : GB Editoria$b2013$1210 $a48 p.$cill. ; 15x15 cm$1215 $aIn copertina uno schizzo di Le Corbusier.$v3 620 $dRoma$3VANL000360 712 $aGB Editoria$3VANV114781$4650 801 $aIT$bSOL$c20240906$gRICA 912 $aVAN00116696 996 $aABACO$9526981 997 $aUNICAMPANIA LEADER 05520nam 2200709Ia 450 001 9911020081403321 005 20200520144314.0 010 $a9786611939458 010 $a9781281939456 010 $a1281939455 010 $a9780470699904 010 $a0470699906 010 $a9780470699898 010 $a0470699892 035 $a(CKB)1000000000555406 035 $a(EBL)366811 035 $a(SSID)ssj0000135026 035 $a(PQKBManifestationID)11134129 035 $a(PQKBTitleCode)TC0000135026 035 $a(PQKBWorkID)10056869 035 $a(PQKB)10226303 035 $a(MiAaPQ)EBC366811 035 $a(CaSebORM)9780470512586 035 $a(OCoLC)441874986 035 $a(OCoLC)832739876 035 $a(OCoLC)ocn832739876 035 $a(Perlego)2757060 035 $a(EXLCZ)991000000000555406 100 $a20080718d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aData mining techniques in grid computing environments /$feditor, Werner Dubitzky 205 $a1st edition 210 $aHoboken, NJ $cJ. Wiley$d2008 215 $a1 online resource (290 p.) 300 $aDescription based upon print version of record. 311 08$a9780470512586 311 08$a047051258X 320 $aIncludes bibliographical references and index. 327 $aData Mining Techniques in Grid Computing Environments; Contents; Preface; List of Contributors; 1 Data mining meets grid computing: Time to dance?; 1.1 Introduction; 1.2 Data mining; 1.2.1 Complex data mining problems; 1.2.2 Data mining challenges; 1.3 Grid computing; 1.3.1 Grid computing challenges; 1.4 Data mining grid - mining grid data; 1.4.1 Data mining grid: a grid facilitating large-scale data mining; 1.4.2 Mining grid data: analyzing grid systems with data mining techniques; 1.5 Conclusions; 1.6 Summary of Chapters in this Volume; 2 Data analysis services in the knowledge grid 327 $a2.1 Introduction2.2 Approach; 2.3 Knowledge Grid services; 2.3.1 The Knowledge Grid architecture; 2.3.2 Implementation; 2.4 Data analysis services; 2.5 Design of Knowledge Grid applications; 2.5.1 The VEGA visual language; 2.5.2 UML application modelling; 2.5.3 Applications and experiments; 2.6 Conclusions; 3 GridMiner: An advanced support for e-science analytics; 3.1 Introduction; 3.2 Rationale behind the design and development of GridMiner; 3.3 Use Case; 3.4 Knowledge discovery process and its support by the GridMiner; 3.4.1 Phases of knowledge discovery; 3.4.2 Workflow management 327 $a3.4.3 Data management3.4.4 Data mining services and OLAP; 3.4.5 Security; 3.5 Graphical user interface; 3.6 Future developments; 3.6.1 High-level data mining model; 3.6.2 Data mining query language; 3.6.3 Distributed mining of data streams; 3.7 Conclusions; 4 ADaM services: Scientific data mining in the service-oriented architecture paradigm; 4.1 Introduction; 4.2 ADaM system overview; 4.3 ADaM toolkit overview; 4.4 Mining in a service-oriented architecture; 4.5 Mining web services; 4.5.1 Implementation architecture; 4.5.2 Workflow example; 4.5.3 Implementation issues 327 $a4.6 Mining grid services4.6.1 Architecture components; 4.6.2 Workflow example; 4.7 Summary; 5 Mining for misconfigured machines in grid systems; 5.1 Introduction; 5.2 Preliminaries and related work; 5.2.1 System misconfiguration detection; 5.2.2 Outlier detection; 5.3 Acquiring, pre-processing and storing data; 5.3.1 Data sources and acquisition; 5.3.2 Pre-processing; 5.3.3 Data organization; 5.4 Data analysis; 5.4.1 General approach; 5.4.2 Notation; 5.4.3 Algorithm; 5.4.4 Correctness and termination; 5.5 The GMS; 5.6 Evaluation; 5.6.1 Qualitative results; 5.6.2 Quantitative results 327 $a5.6.3 Interoperability5.7 Conclusions and future work; 6 FAEHIM: Federated Analysis Environment for Heterogeneous Intelligent Mining; 6.1 Introduction; 6.2 Requirements of a distributed knowledge discovery framework; 6.2.1 Category 1: knowledge discovery specific requirements; 6.2.2 Category 2: distributed framework specific requirements; 6.3 Workflow-based knowledge discovery; 6.4 Data mining toolkit; 6.5 Data mining service framework; 6.6 Distributed data mining services; 6.7 Data manipulation tools; 6.8 Availability; 6.9 Empirical experiments; 6.9.1 Evaluating the framework accuracy 327 $a6.9.2 Evaluating the running time of the framework 330 $aBased around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students inte 606 $aData mining 606 $aComputational grids (Computer systems) 615 0$aData mining. 615 0$aComputational grids (Computer systems) 676 $a004/.36 701 $aDubitzky$b Werner$f1958-$01602621 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020081403321 996 $aData mining techniques in grid computing environments$94417687 997 $aUNINA