LEADER 01066nam0 2200313 i 450 001 CAG1832086 005 20170908093307.0 010 $a9788884676405 100 $a20120725d2010 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $a˜L'Âœedera$fGrazia Deledda$gedizione critica a cura di Dino Manca 210 $aCagliari$cCentro di Studi filologici sardi$aCUEC$d2010 215 $aCLX, 316 p.$d19 cm 225 | $aScrittori sardi 300 $aIn custodia. 410 0$1001CAG0028885$12001 $aScrittori sardi 700 1$aDeledda$b, Grazia$3CFIV000071$4070$0196214 702 1$aManca$b, Dino$3CAGV007923 790 1$aDeledda$b, Gracja$3CFIV003949$zDeledda, Grazia 801 3$aIT$bIT-NA0079$c20120725 850 $aIT-NA0079 912 $aCAG1832086 950 0$aBiblioteca Nazionale Vittorio Emanuele III$d BNMAGAZZINO 2012 A 704$e BNDO 0012215945 B 1 v.$fT $h20120725$i20120727 977 $a BN 996 $aEdera$999106 997 $aUNISANNIO LEADER 05376nam 2200685 450 001 9910787905603321 005 20200520144314.0 010 $a0-12-802091-1 035 $a(CKB)2670000000578822 035 $a(EBL)1875436 035 $a(SSID)ssj0001432549 035 $a(PQKBManifestationID)11778771 035 $a(PQKBTitleCode)TC0001432549 035 $a(PQKBWorkID)11406593 035 $a(PQKB)10787791 035 $a(Au-PeEL)EBL1875436 035 $a(CaPaEBR)ebr10997047 035 $a(CaONFJC)MIL666050 035 $a(OCoLC)900291708 035 $a(CaSebORM)9780128020449 035 $a(MiAaPQ)EBC1875436 035 $a(PPN)189085762 035 $a(EXLCZ)992670000000578822 100 $a20150108h20152015 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aData architecture $ea primer for the data scientist : big data, data warehouse and data vault /$fW. H. Inmon, Dan Linstedt ; Steven Elliot, executive editor ; Mark Rogers, designer 205 $a1st edition 210 1$aAmsterdam, Netherlands :$cMorgan Kaufmann,$d2015. 210 4$d©2015 215 $a1 online resource (378 p.) 300 $aIncludes index. 311 $a0-12-802044-X 311 $a1-322-34768-9 327 $aCover; Title Page; Copyright; Dedication; Contents; Preface; About the authors; 1.1 - Corporate data; The Totality of Data Across the Corporation; Dividing Unstructured Data; Business Relevancy; Big Data; The Great Divide; The Continental Divide; The Complete Picture; 1.2 - The data infrastructure; Two Types of Repetitive Data; Repetitive Structured Data; Repetitive Big Data; The Two Infrastructures; What's being Optimized?; Comparing the Two Infrastructures; 1.3 - The "great divide"; Classifying Corporate Data; The "Great Divide"; Repetitive Unstructured Data; Nonrepetitive Unstructured Data 327 $aDifferent Worlds1.4 - Demographics of corporate data; 1.5 - Corporate data analysis; 1.6 - The life cycle of data - understanding data over time; 1.7 - A brief history of data; Paper Tape and Punch Cards; Magnetic Tapes; Disk Storage; Database Management System; Coupled Processors; Online Transaction Processing; Data Warehouse; Parallel Data Management; Data Vault; Big Data; The Great Divide; 2.1 - A brief history of big data; An Analogy - Taking the High Ground; Taking the High Ground; Standardization with the 360; Online Transaction Processing 327 $aEnter Teradata and Massively Parallel ProcessingThen Came Hadoop and Big Data; IBM and Hadoop; Holding the High Ground; 2.2 - What is big data?; Another Definition; Large Volumes; Inexpensive Storage; The Roman Census Approach; Unstructured Data; Data in Big Data; Context in Repetitive Data; Nonrepetitive Data; Context in Nonrepetitive Data; 2.3 - Parallel processing; 2.4 - Unstructured data; Textual Information Everywhere; Decisions Based on Structured Data; The Business Value Proposition; Repetitive and Nonrepetitive Unstructured Information; Ease of Analysis; Contextualization 327 $aSome Approaches to ContextualizationMapReduce; Manual Analysis; 2.5 - Contextualizing repetitive unstructured data; Parsing Repetitive Unstructured Data; Recasting the Output Data; 2.6 - Textual disambiguation; From Narrative into an Analytical Database; Input into Textual Disambiguation; Mapping; Input/Output; Document Fracturing/Named Value Processing; Preprocessing a Document; Emails - A Special Case; Spreadsheets; Report Decompilation; 2.7 - Taxonomies; Data Models and Taxonomies; Applicability of Taxonomies; What is a Taxonomy?; Taxonomies in Multiple Languages 327 $aDynamics of Taxonomies and Textual DisambiguationTaxonomies and Textual Disambiguation - Separate Technologies; Different Types of Taxonomies; Taxonomies - Maintenance Over Time; 3.1 - A brief history of data warehouse; Early Applications; Online Applications; Extract Programs; 4GL Technology; Personal Computers; Spreadsheets; Integrity of Data; Spider-Web Systems; The Maintenance Backlog; The Data Warehouse; To an Architected Environment; To the CIF; DW 2.0; 3.2 - Integrated corporate data; Many Applications; Looking Across the Corporation; More Than One Analyst; ETL Technology 327 $aThe Challenges of Integration 330 $aToday, the world is trying to create and educate data scientists because of the phenomenon of Big Data. And everyone is looking deeply into this technology. But no one is looking at the larger architectural picture of how Big Data needs to fit within the existing systems (data warehousing systems). Taking a look at the larger picture into which Big Data fits gives the data scientist the necessary context for how pieces of the puzzle should fit together. Most references on Big Data look at only one tiny part of a much larger whole. Until data gathered can be put into an existing framework or a 606 $aData warehousing 606 $aBig data 615 0$aData warehousing. 615 0$aBig data. 676 $a005.745 700 $aInmon$b W. H.$01483892 702 $aLinstedt$b Dan 702 $aElliot$b Steven 702 $aRogers$b Mark 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910787905603321 996 $aData architecture$93702221 997 $aUNINA LEADER 03104nam 2200697 450 001 9910798171903321 005 20231110212925.0 010 $a1-118-72704-5 010 $a1-118-72712-6 035 $a(CKB)3710000000603982 035 $a(EBL)4415499 035 $a(SSID)ssj0001614352 035 $a(PQKBManifestationID)16341433 035 $a(PQKBTitleCode)TC0001614352 035 $a(PQKBWorkID)14914926 035 $a(PQKB)10980383 035 $a(MiAaPQ)EBC4415499 035 $a(DLC) 2015041906 035 $a(Au-PeEL)EBL4415499 035 $a(CaPaEBR)ebr11161366 035 $a(CaONFJC)MIL898845 035 $a(OCoLC)941696436 035 $a(MiAaPQ)EBC7103998 035 $a(Au-PeEL)EBL7103998 035 $a(JP-MeL)3000111248 035 $a(EXLCZ)993710000000603982 100 $a20151021d2017 uy| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aLecture notes$iGastroenterology and hepatology /$fStephen Inns, Anton Emmanuel 205 $aSecond edition. 210 1$aChichester, West Sussex ;$aHoboken, NJ :$cWiley Blackwell,$d2017. 215 $a1 online resource (291 p.) 225 0 $6880-04$aLecture notes 300 $aIncludes index. 311 $a1-118-72812-2 327 $aEvaluation of nutritional statusNutritional support; Parenteral nutrition; Enteral nutrition; Physiology of starvation; The refeeding syndrome; Intestinal failure; Chapter 5 Gastrointestinal infections; Oral infections; Oesophageal infections; Helicobacter pylori infection; Acute gastroenteritis; Intestinal tuberculosis; Miscellaneous gut infections; Anal infections; Gut symptoms in HIV infection; Chapter 6 Gastrointestinal investigations; Structural tests; Physiological tests; Part II Gastrointestinal Emergencies; Chapter 7 Acute gastrointestinal bleeding 327 $aSmall intestinal bacterial overgrowthBile acid malabsorption; Whipple's disease; Protein-losing enteropathy; Intestinal infections; Small intestine tumours; Miscellaneous intestinal disorders; Chapter 15 Small and large bowel disorders; Inflammatory bowel diseases (IBDs); Microscopic colitis; Functional gastrointestinal disorders; Ischaemia of the gut; Radiation enterocolitis; Chapter 16 Colon; Colorectal tumours; Miscellaneous colonic disorders; Chapter 17 Anorectum; Haemorrhoids; Anal fissure; Anal fistula; Anal pain and itch; Anal cancers; Rectal prolapse; Solitary rectal ulcer syndrome 327 $aChapter 18 Pancreatic diseases 410 0$aNew York Academy of Sciences 517 1 $aGastroenterology and hepatology 606 $aGastroenterology 606 $aHepatology 615 0$aGastroenterology. 615 0$aHepatology. 676 $a616.3/3 686 $a616.3/3$2njb/09 686 $a493.4$2njb/09 686 $aWI 140$2njb/09 700 $aInns$b Stephen$01576841 702 $aEmmanuel$b Anton 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910798171903321 996 $aLecture notes$93854949 997 $aUNINA