Chapter 24: Advanced Data Types and New Applications

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+ Okaor OF: Odrrwed Dela ‏مه سور‎ Dow ‏امه‎ Vewpord Derr pote ond ‏اه( ساموت‎ Ondoeds ‏له‎ لت لوط لت ره |۱۳ Gyetre Oocowytr, Oe. een ©Sbervehnts, Cork ced Cnakershe 

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عععوطد۵() ۰ و BE Ohie covet ‏لت وا لت سول‎ reuly uta pot ia oe (at he “pure koe), ‏پ‎ ‎t‏ تانب ,للم ‎hove fated foes whe they are‏ تا اس ا ام اه مه و ‎represedied‏ ‎© OVhe trewwton thee Por ‏مایت تسد لو وه با با و و‎ the Port is currect wikia the dotcbase sysiew. ‎11 4a0 tewpord relatos, euch tuple hus oo ussvctoted thee whe tis tour; the the way be ether ald toe or ‏.صاصم مس‎ ‎BO bHtewpord relatos stores bots void cad tracsuntivg fe7e. ‎Od. ere ©Sbervehnts, Cork ced Cnakershe‏ ,تن 6 تیه ‎ 

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to 1999/1/24 11:30 2000/8/8 10:00 2000/9/5 0 2000/5/1 16:00 1999/1/1 0 1999/1/24 11:30 2000/6/2 15:30 2000/8/8 10:00 2000/9/5 0 1999/7/5 0 balance branch-name Downtown. Downtown Mianus Mianus Mianus Brighton “Deopord query ‏وا رما موم‎ proposed to ‏رام‎ oodetor oF toe os ued os ioe ene vekted queries. @eedrwr Gyetre Oocowytr, Oe. 

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+ 1 OpeoP ۰ ‏ص06‎ Bde: Pour dite Por the peor ((-9999), two dts Por the «ora (I-12), cord uw cht Por the date (I-20). BE teow! tue chtts Por the how, tive chile ‏سل طوت سا و‎ chet Por ther spre phe ard Hornet tore, ۲ tevestany: the Picks oP date ord toe, wits six Pracicadd chats Por the seooads Piet. BD koes oe spertied in he Daversdl Onordheted Dave, cbbrevicted DDO (Prow te Precck); suppers teow wik teow zou. Berd: refers too ported oP tev (e.1,, © skys oad & hers), thot ‏لصو و رود‎ koe hes this period starts; ook were curtis be ‏لح‎ a spam. @eedrwr Gyetre Oocowytr, Oe. eee ©Sbervehnts, Cork ced Cnakershe 

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Tecppord Query beaquages Bl Oredmuee precede, puerkips, ond protons va ike rien ide. ۲ ‏له با موه ها‎ oa tur ‏رح‎ ۲ que o stade (possbhy exp) ‏سمه‎ ‎the ‏مد اه مس‎ iniervdds way or cay cei be ot sient iter. ‎oP the tupkes rot ane vol of‏ سم ۱ ‎of ve‏ ما ما که وتا ‎koe |, wik he tkoe-ierid otcbules proerted mu.‏ ‎oldies‏ بو اه نمی لیا ‎Proxy the‏ ره با بط ما و ی و ا ‎rekaio.‏ لو ‎tuples ta he‏ ‏خن مس ‎reels the‏ حصا جا وه خن امرس با تم ای ۴ ‏اد ,ری ا سود ۱ لصو ۱ مایت مت او لا چاو نو مب ‎ts discarded‏ ‎ ‏سا0 لح 0 لا سواه 1 ومه ‎@eedrwr Gyetre Oocowytr, Oe.‏ 

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+ iPacopord ‏ی‎ 0 0 ‏الي‎ ‎padkbr Sppaend ‏معطمو‎ ,۵ ۱ موه ما و بالات ‎Y‏ 9 ور روسو سيل ‎B® teopord Rewtond‏ طلست ام ‎guy he‏ اه مان ‎FoF R, dl‏ حص ايسا أن ‎a.‏ ۲ ۵0:99 Pat? (CAL/Pewperd) © 0 proposed extexowa GCQALA999 1 keprove support oF ‏او‎ cht. سا0 لح 0 لا سواه 1 ومه ‎@eedrwr Gyetre Oocowytr, Oe.‏ 

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‎wad Cevquphic Oubwee‏ دهم66) 

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مه( موی لب لو + ‎aed suport‏ ,هرقن و لخاد بط بت یل لس لا ‎kev.‏ مرت اب ری لح بل مت ما ‎۱ ‎Por provessien spud okt queries. ‎۲ ‏وین‎ @rded Desiqa (OBO) cuichuses store devia torardion chou hour ‏لو اه وا وت ما ات سا نو لسن بو مان‎ vine ‎۳ ‏تما بطارو وی بو تال رو وتا‎ (ec, ware): oe ode ‏0ك‎ ‎00 Gyetre Oocowytr, Oe. ene ©Sbervehnts, Cork ced Cnakershe 

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+ QRepreseed of Beoweric IePorwun B Ootne powers prwinwty oo be represeuied ‏ان لت و و معط ورن‎ Be Represet a hoe seemed by the cookies oP tty eanbrctcty. ۱ ‏موجه و ما ارجام نوا سوه ماو‎ oP secret ‏-دس-ب«»«ِ«۰‎ ‎۱ rack seemed we 9 separde Kipke that dbo carries wat the khectP ier oP tor ‏امه هه 60) سصه‎ ee rood). ۱ ‏اس اسان‎ © Lit oP vertices ta order, steers verti ty the sun oe br eck verte, © Represet boenkory eckes ce separa: Nps, wil wack ovutatcary RheatPer oP ter pubxpra, or © Doe textos — dead polars ‏صو‎ trees © Doe the polar Rewer wil mack oP te tracey. ‎Od.‏ ,تن 6 تیه 

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(od.y2), 2y2)) LOL), (42,92), (3,y3)] [xh yl), 62y2), 3,99), Oh y8), O5.y5)] [(xL,y1), (x2,y2), (x3,y3), ID1} [(xLy1), (<3,y3), (bya), ID) [(x1,y1), (x4,y4), (x5,y5), ID1} object representation سا0 لح 0 ا سا0 1 موه ‎Bed.‏ تن سس 6 تیه 

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+ Qepresecttoa oP Orowets “‘kPorwdiva (Ovu.) © Represectaiva of points ood hoe seyerent io O-O ‏وت‎ to O-D, except trot porate have oo extra 2 oowpoort © ‏لا ,هه مد مب مت توا رام روشاه مس‎ trtcracqukaticy ‏وا‎ ۲ @hercaive: List their Pures, euck of ‏اه مه وه طلست واه رمپاسن مج وان‎ hick side oP the Pace ‏.ممص رامع لجع‎ سا0 لح 0 لا سواه 1 و ‎@eedrwr Gyetre Oocowytr, Oe.‏ 

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حعحهط() مپبوو() BH Represeut denn onan w cbeue (ewerndy werent ober): hie ‏بط رما ی اه بط مها مین‎ devin is sintered. 9 ‏مس‎ phere! pomir, bore, trees, repeater, poh. ۳ ‏لو نطو ول‎ Broce kore oboe vit mio, ‏ورن سل لت رسیم‎ ‎or‏ وه واه وه سا اه تم م۱ مسال لجن رس رم بو ‎spheres, overs, aed mubpiks,‏ یس وی ‎BD retrnwe ‏اوه امس مت وت‎ ce a set of ‏ارده‎ object. ‏سا0 لح 0 لا سواه 1 مدهو ‎Od.‏ ,تن 6 تیه 

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(a) Difference of cylinders (b) Union of cylinders (a) OP Rereue of ohare ‏لو هه (ما)‎ Bl Week cubisey uby ore axrspurd ‏مسمس وكام‎ ubout vbpus (ep, ean ete winter, ovo, etc.) ۴ ‏او نوی تست ول‎ © xt, pipes chink! ool Kiersen, wires shovkd oot be too cee to pack ober, eto. سا0 لح 0 لا سواه 1 موجه ‎@eedrwr Gyetre Oocowytr, Oe.‏ 

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+ @eoerophic Das © Retr ‏با خر میت ول‎ waps or pixel swaps, it tun or wore dkvrosivas. © Cxanple C-O ester toage! cutee tage oP cloud cover, where ea pixel stores the cloud ‏نام و وا قاطا‎ area. © Oddiiccd dveusices witht tudude the tewepercure of dPPereo obtudes ot iPPercot revo, or weasureweuis fohed of dP erect potato tie. ۲ ‏ری ال مرت(‎ de oot store roster data. سا0 لح 0 لا سواه 1 هه ‎Od.‏ ,تن 6 تیه 

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@evqrphic Ota (Ovu.) © Oevir date ore coustrunted Prow basic ‏تاه موی‎ pois, he ‏مهد‎ iheades, ood oer ‏شوه له سس سا اس‎ speheres, vubpids, ocd vier polvhedrows to three dkoecsizas. © Oevior Pow ‏مدل و مس صا لصت وكام‎ © Roads co be oocsidered os two-dkorusivedd ood represedied by bees ood © Gowe Pectures, such us rivers, way be represeued ether or comple punves pros cowplex pores, depeudtey mo whether their uty relevent. © ‏سم‎ such ws reqs ond hhes coo be depicted os poly. سا0 لح 0 لا سواه 1 موجه ‎Od.‏ ,تن 6 تیه 

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+ @pphotow oP Beoqruphis Dota ۲ ‏ول او جر ام‎ © ‏ی لس ۳ مد وت‎ ۱ cetwork toPorwativa Por power, telephours, woter supply, ood seu ۲۱ ‏اون‎ caviqaicg sysiews store nPorwuiva dboul rads ond services Por the use oP drivers: ۶ ‏وعدملم صمت سل سمل رت بل سوق‎ © Oowspard dita! ‏وه ری‎ streets, speed his, troPPic coayestion © Obbd ‏میت(‎ Gystew (BPG) vat - ‏اما مومت ات‎ Prow GAG suteltes to Pod the cone locaiog of user wih oct anurans OP tecs oP weters. بهل ‎weed io vehicle cavigqaion systews os well os‏ باس و .ع مادص رون سکن سا0 لح 0 لا سواه 1 موجه ‎Gyetre Oocowytr, Oe.‏ ۱۳| 

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Gpord Queres © Qraness queres request objevis tho ke ‏سا لته وچ‎ © Qrcrest veighbor queries, yveu 9 potdt or oc objet, Pied the ueurest object ‏له مسب اه سا‎ © Region queries ded wih sputd reyioes. e.g, ush Por objects thot he portal or Pauly toside 0 speriPed reyioa. © Qeeres hot cow pie tntersevioas or usivas oP reqivas. © Gpottal iota oP tee spaidl reticos wis the locotica ployioy the role oP jpict ttre. ‎Od. een ©Sbervehnts, Cork ced Cnakershe‏ ,تن 6 تیه 

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‎Queries (Ovd.)‏ دهم۵) ‎19 Gadd dota 6 ipod) queried usin a yrophicd query keeneee) resus ore ‏ی اون وه لوط سل‎ ‏ام ‎the‏ ی تا تا لا ‎© Cxtewire of GDL wik ubsirant dota types, suck us bees, polos od bit swaps, hove beew proposed te interPace with ‏او‎ ‎ced esr sped pero‏ جیهم لیب پی او مرو ‎or pverkws).‏ ای بطم لهس موه سسن ۶ جمدم ارس له له بو من سم ‎ ‏سا0 لح 0 لا سواه 1 موجه ‎Od.‏ ,تن 6 تیه 

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۰ ۵۵4 جه سل + ۲ ‏لح مه راو - لعا‎ ۳ tedextery ‏وله‎ dicvecsiccs.. ۲ Cock bevel Pu fed tree portions the spare fair he. © choose coe dkeewsiva Por ‏امس با اه من‎ level oP the tree. © choose carer dhoeusizas Por porticciay tr ordes of he cent kevel cad soo, oye through the dveusivas. © dock ue, upproxtouiely hd oP the picts stored te the sub-irer Poll a vor sick soo hol ‏مس‎ the other. © Ponticgiay stops wheo 0 oode hos fess foo yived woxteue ‏اون‎ oP ‏جامامم‎ ام اس ‎codes Por‏ اجان ول تاه وا لمم[ ‎tree exteuds the‏ )لمجا 710197 18 .سعصصاد موه و تسام له سا0 لح 0 لا سواه 1 موجه ‎Od.‏ ,تن 6 تیه 

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+ Ove oP Opwe by ak-d Tree 3 ۴ Goch ie ia he Prue (ober thaw he outside box) corresponds to code ft te edie © the woxkrury cube oP potas tro ech ade hos bera set tC. BP he wunbertay of the hoes to the Pique todicates the bevel oP the tree of wwhick the correspoudkny ude uppeurs. سا0 لح 0 لا سواه 1 مومه ‎@eedrwr Gyetre Oocowytr, Oe.‏ 

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QDvewa oP Ope by Quadress + Beck ae of a naniter & ceypetaed wih a repkeeadar reqva oP spare) he top ool mona uth he ‏فيصم مت‎ op ی لاه موس سا با مریم ‎Cok woke ake dies ty‏ ۱ ۰ ead oy ot ۵ Lee canky hove between zery onl oo ‏هن خن ای مهم لا‎ (ort ty Ct excep). سا0 لح 0 لا سواه 1 مهو ‎@eedrwr Gyetre Oocowytr, Oe.‏ 

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(0) طسو BPR quedbee! stores points; spare ts divided bused va reqous, ruher thos oo the ‏سم‎ set oF potas stored, ‎Reqoa quieres sire ary (roster) KPorwatos.‏ لا ‎De ty a eo onde te the array Vokes te the reqivg thot ft eovers ore the‏ © ‎save. Otkeruise, this subdivided Purther toto Pour chidecd oP equal aed, veo fs therePore ‏له اس من‎ ‎© Gk ade ‏خا موه و وا ط موم‎ dues. ‎© ۳ ‏و اهر یی اه عم 0 موه وولو‎ stage aay ‏راو‎ or have ‏نی‎ array pieces, ll oP uhick have the ‏.صا وه‎ ‎© Cxtewirw of kedirees ood PR quadrees have beeu proposed te telex bo ‏مان اجه موس‎ ‎© Require splitay ‏صادا جدم عام |صاسجمجد‎ pieces of portiizaiog bouadaries » Gawe seueeulpolyyro way be represedied of severd leu? odes: ‏سا0 لح 0 لا سواه 1 همومه ‎Od.‏ ,تن 6 تیه 

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Q-Trees Bo Rtrees we o Once eusivad exteusiva of 3 trees, usePul Por tedentay sete ‏اه ای عم ان‎ polo. اه | جصس د *8) له ‎trees‏ لاب اوه همست و ‎oP‏ مات ‎the‏ اس :صلم وه 1# ‎sree oode to or‏ + 8) جاص اج او( مه ‎thot is,‏ هه امس (6 < 0) سس امس با رات ی( ۲ ۶ ‏تلم‎ ۲ 0 < 9 ۰ straghPorwerd, thouds R-rees work well vay Por ‏امه راعشا‎ 0 سا0 لح 0 لا سواه 1 مومه ‎Od.‏ ,تن 6 تیه 

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BO revtreuiar bovadiay box te ussocided wih ‏ام‎ tree ade. © ‏اه و سا موه‎ onde & a winkou sized revionde thot o7ctoices ol the revtomdes|polvyras ussucidied wits the teu? ode. © Dke ‏خلت له ها مها‎ a areed® arde pootaies the bouadey bo assorted ust oll tts chided. Oowrrden box oP cde server cr tis hey ia te pare cde (P coy) ۶ ‏اجره له له و و مسا و سا وله‎ © pene stored poly fo vor onde, oer the bourrdery bo of the ode oust ‏مات‎ عبس ی لا اه نا ما تا ها باه تاه موه ۳ © وی ‎stored aly‏ مار و ‎quadirees siwwe‏ دع ‎a‏ ‏مه مه يه 

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Oxavpls Q-Tree © set ok reorder (ook ber) orn he bourse (chrkend foe) of the codes of om (R-tree Por te ‏ام‎ ۱ (R= tree te shen ou bor rnb. سا0 لح 0 لا سواه 1 مومه ‎Od.‏ ,تن 6 تیه 

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+ Geavk ia Q-Trees BD o Pron chic tow (revkandenlpph apse) tteroectny (verkee) 0 aes ery ‏رل‎ do the Polowicg, stertoy Prow the root ade: ‎1P the code alec? ode, vulput the dota tiews whose hepe totersent the‏ و .كص لكادم بصصي دجيف ‎© Glee, Por euch child of the curred ‏جملا وه ما مها ات طلوه‎ spery porilredioa, recursively search the chi ‎© Coc be very eh hited in worst vase swe wuliple puke way ceed to be searched ‎© btworks uoveptaby i pravice. ‏له مومس طلسم ما وا عم ‎Grople exteusivas of seurck‏ © موسر ‏سا0 لح 0 لا سواه 1 وومةه ‎Od.‏ ,تن 6 تیه 

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+ (rewrtra ta RT rows © 2189 ‏حصا‎ a dota tew! © Grad a tec? ty store it, ood odd itt the feo ۲ Do Pied teu, Polow a chid (iP coy) whose bovediey bor cratic bouediag box oF dota tec, ebse ohid whose overtop wis dot tec ‏موی بابلا‎ © Aerie overPawe by oplis (ve ia B+ -reen) » Oph procedure ts dPPeredt boank (see bebw) © Odhet bourke boxes starter Proxy be ke uawarde 0 © )8 ‏و ند مد طه الاو مه اه وجوج حلط :ادم‎ suck thot the booed; boxes hove ‏موه اه جات‎ ۱ Dhis is a heuristic. Blercuives the ctotruc overtop ane ‏الوم‎ ‎© ‏سا بل‎ “best” splits expeusive, use heuristics tosteud ۱ Gee cent side سا0 لح 0 لا سواه 1 جوهو ‎Od.‏ ,تن 6 تیه 

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+ Oplacg aa R-Tree Dede 11 Quedrate oplt duides the euiies ‏جم من ما طلجه و وا‎ ordes us Polos ‏و مج نت ی و مر لو‎ ‎her bv boc oP the tir ward fro er‏ ما اجب ‎space (area oF bovradar box — unr of reas oP ee‏ لو و ‎vues)‏ ‎©. Che these euler hoo ce odes ‎) Repededy Prod the eoiry wits “oaxterucy prePereaze” Por oe oP the 7 sew codes, ood ussiqa the euiry ty tat ude ‎* OrePercure of un euiry too ode fp the feereuse fo orca of boundary box Pike putty te okded ty the other ude ‎0. Gtop wheo kolP the eutries hove bera odded to vor ode (hea ‏ددص مادص‎ cules ty the ver ode © Chewer heew pit heuristic works fa tive ‏ری و امس و وا‎ © Cheaper but yeurrutes skchiy worse splits. ‏سا0 لح 0 لا سواه 1 وومةه ‎Od.‏ ,تن 6 تیه 

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Oetstay ta Q-Treee © Oetica of oo ‏روش‎ it oo R-tree doce ‏اس‎ the o (Bree delet. © Aa case oP uederPul onde, borrow euiies Prow a sible P possible, cbse ‏له ماه مس‎ ۱ ‏ات‎ ‎wate, theo reteerts ol cures 4 سا0 لح 0 لا سواه 1 وومةه ‎Od.‏ ,تن 6 تیه 

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Qubweda Oabwes © V0 pride suck dotobose Puortioos us irdextoy ood cousisteary, fis desirable to store wulticoedia dota too dotcbase © ‏تماد مص ام‎ thew outside the dotdbose, tao Pe systew BD he ‏.مص و ماه ها عم من لد‎ ‏للم سا اس ره لوط رامق‎ by spevtd index siruckres. ۲ ‏مس(‎ quorcuieed steady retievd rotes Por cocikrumus-cedk deta. سا0 لح 0 لا سواه 1 مهو ‎Od.‏ ,تن 6 تیه 

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Qnitweda Oca Porwas © Gpre ond recent wditvedta dota te ooepressed Pow © IPCC ood 91 the cost widely used Porcuts Por ‏.ملك وم‎ © OPEC stared Por vider deta ‏سوه و مومه جعفلموو جه جك‎ ‏ما عسوا نام‎ achieve a oredr deyree oP ‏وی‎ B OPECE~1 qty expat to OG vider tape. © sires u winie oP OO -Preweper-seored video ord aude fe epproatraiely 19. ۲ OPCE-C desiqaed Por dyid broadcast spstews ‏اه من اما له‎ edible lees oF vider quali. © Cowpresses (| wioute oP oudio-vider to approxi IP DB. © Geverd chercdives oP ‏تست طلمی‎ © DPEC-A Laer 9 (DPS), ReckPudir, Dido Deda ‏رام‎ سا0 لح 0 لا سواه 1 مومو ‎Od.‏ ,تن 6 تیه 

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Covtqww-Deda Oata Oost koportedt types ore vider ord odio dot. © Chorwterted by high dota vokever oad rechioe ePorwotiordelvery ‏اجر‎ © Oct cst be delivered subPictealy Post trot there are on gaps it the oui or vider. © Oca cst be delvered of a roe thot dees ont couse ‏لاه‎ oP systew ‏یال‎ ای سا اس مس مل سل وت ملق ۶ ۱ ‏لاب رام طسو ری ها منطو بح لو موم و ان ملس‎ te ‏لام‎ 4 سا0 لح 0 لا سواه 1 مومه ‎Od.‏ ,تن 6 تیه 

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Otdev Gervers B Okboedewadd sysiews deliver vide Prow ceuird vider servers, unrvss a ‏امه و9 امه‎ حص روصت هلوت من ‎Oxst‏ © سل ره :مرو سل ‎ore bused va‏ ولمم لش من ۲ جوم و ‎respouse‏ سا ام لت ول مود ۳ ‏مد لس‎ or stored oa severd deke (RO 1D cob nection), or oa tery store Por kes Prequedly accessed skis. Weed-eud terwicab = used te view wultteredia dat © Cs o TOs attacked too sual, toexpeusive computer riled a setioy box. سا0 لح 0 لا سواه 1 هوهو ‎Od.‏ ,تن 6 تیه 

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+ GrokoaDused Rerevd Caanoples oP sivtorty bused retrieval © Ptr dota: Dive pictures or keones thot ore sth) dPPeredt os represeuted ier the database way be ooustdered the suxpe by ower. © ‏اوه رام رو‎ desiqes Por ‏مت‎ a ew trdeworh. © ude dota: Gpeech-bosed wer toterPaves olow the user to yive 0 ooo or death) a dota tear ‏.مسد برط‎ © Gig, test user ioput agpivet stored coeur. ۲ ‏وه لو سس مجنا مامل مجلا متها ه را ما تا‎ ict the ‏لول‎ سا0 لح 0 لا سواه 1 مومه ‎Od.‏ ,تن 6 تیه 

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Chapter 24: Advanced Data Types and New Applications  Temporal Data  Spatial and Geographic Databases  Multimedia Databases  Mobility and Personal Databases 1 Database System Concepts, 5th Ed. 24.1 ©Silberschatz, Korth and Sudarshan Time In Databases  While most databases tend to model reality at a point in time (at the ``current'' time), temporal databases model the states of the real world across time.  Facts in temporal relations have associated times when they are valid, which can be represented as a union of intervals.  The transaction time for a fact is the time interval during which the fact is current within the database system.  In a temporal relation, each tuple has an associated time when it is true; the time may be either valid time or transaction time.  A bi-temporal relation stores both valid and transaction time. 2 Database System Concepts, 5th Ed. 24.2 ©Silberschatz, Korth and Sudarshan Time In Databases (Cont.)  Example of a temporal relation:  Temporal query languages have been proposed to simplify modeling of time as well as time related queries. 3 Database System Concepts, 5th Ed. 24.3 ©Silberschatz, Korth and Sudarshan Time Specification in SQL-92  date: four digits for the year (1--9999), two digits for the month (1--12), and two digits for the date (1--31).  time: two digits for the hour, two digits for the minute, and two digits for the second, plus optional fractional digits.  timestamp: the fields of date and time, with six fractional digits for the seconds field.  Times are specified in the Universal Coordinated Time, abbreviated UTC (from the French); supports time with time zone.  interval: refers to a period of time (e.g., 2 days and 5 hours), without specifying a particular time when this period starts; could more accurately be termed a span. 4 Database System Concepts, 5th Ed. 24.4 ©Silberschatz, Korth and Sudarshan Temporal Query Languages  Predicates precedes, overlaps, and contains on time intervals.  Intersect can be applied on two intervals, to give a single (possibly empty) interval; the union of two intervals may or may not be a single interval.  A snapshot of a temporal relation at time t consists of the tuples that are valid at time t, with the time-interval attributes projected out.  Temporal selection: involves time attributes  Temporal projection: the tuples in the projection inherit their time-intervals from the tuples in the original relation.  Temporal join: the time-interval of a tuple in the result is the intersection of the time-intervals of the tuples from which it is derived. It intersection is empty, tuple is discarded from join. 5 Database System Concepts, 5th Ed. 24.5 ©Silberschatz, Korth and Sudarshan Temporal Query Languages (Cont.)  Functional dependencies must be used with care: adding a time field may invalidate functional dependency  A temporal functional dependency x  Y holdson a relation schema R if, for all legal instances r of R, all snapshots of r satisfy the functional dependency X Y.  SQL:1999 Part 7 (SQL/Temporal) is a proposed extension to SQL:1999 to improve support of temporal data. 6 Database System Concepts, 5th Ed. 24.6 ©Silberschatz, Korth and Sudarshan Spatial and Geographic Databases Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use 7 Spatial and Geographic Databases  Spatial databases store information related to spatial locations, and support efficient storage, indexing and querying of spatial data.  Special purpose index structures are important for accessing spatial data, and for processing spatial join queries.  Computer Aided Design (CAD) databases store design information about how objects are constructed E.g.: designs of buildings, aircraft, layouts of integratedcircuits  Geographic databases store geographic information (e.g., maps): often called geographic information systems or GIS. 8 Database System Concepts, 5th Ed. 24.8 ©Silberschatz, Korth and Sudarshan Represented of Geometric Information  Various geometric constructs can be represented in a database in a normalized fashion.  Represent a line segment by the coordinates of its endpoints.  Approximate a curve by partitioning it into a sequence of segments   Create a list of vertices in order, or  Represent each segment as a separate tuple that also carries with it the identifier of the curve (2D features such as roads). Closed polygons  List of vertices in order, starting vertex is the same as the ending vertex, or  Represent boundary edges as separate tuples, with each containing identifier of the polygon, or  Use triangulation — divide polygon into triangles  Note the polygon identifier with each of its triangles. 9 Database System Concepts, 5th Ed. 24.9 ©Silberschatz, Korth and Sudarshan Representation of Geometric Constructs 10 Database System Concepts, 5th Ed. 24.10 ©Silberschatz, Korth and Sudarshan Representation of Geometric Information (Cont.)  Representation of points and line segment in 3-D similar to 2-D, except that points have an extra z component  Represent arbitrary polyhedra by dividing them into tetrahedrons, like triangulating polygons.  Alternative: List their faces, each of which is a polygon, along with an indication of which side of the face is inside the polyhedron. 11 Database System Concepts, 5th Ed. 24.11 ©Silberschatz, Korth and Sudarshan Design Databases  Represent design components as objects (generally geometric objects); the connections between the objects indicate how the design is structured.  Simple two-dimensional objects: points, lines, triangles, rectangles, polygons.  Complex two-dimensional objects: formed from simple objects via union, intersection, and difference operations.  Complex three-dimensional objects: formed from simpler objects such as spheres, cylinders, and cuboids, by union, intersection, and difference operations.  Wireframe models represent three-dimensional surfaces as a set of simpler objects. 12 Database System Concepts, 5th Ed. 24.12 ©Silberschatz, Korth and Sudarshan Representation of Geometric Constructs (a) Difference of cylinders (b) Union of cylinders  Design databases also store non-spatial information about objects (e.g., construction material, color, etc.)  Spatial integrity constraints are important.  E.g., pipes should not intersect, wires should not be too close to each other, etc. 13 Database System Concepts, 5th Ed. 24.13 ©Silberschatz, Korth and Sudarshan Geographic Data   Raster data consist of bit maps or pixel maps, in two or more dimensions.  Example 2-D raster image: satellite image of cloud cover, where each pixel stores the cloud visibility in a particular area.  Additional dimensions might include the temperature at different altitudes at different regions, or measurements taken at different points in time. Design databases generally do not store raster data. 14 Database System Concepts, 5th Ed. 24.14 ©Silberschatz, Korth and Sudarshan Geographic Data (Cont.)  Vector data are constructed from basic geometric objects: points, line segments, triangles, and other polygons in two dimensions, and cylinders, speheres, cuboids, and other polyhedrons in three dimensions.  Vector format often used to represent map data.  Roads can be considered as two-dimensional and represented by lines and curves.  Some features, such as rivers, may be represented either as complex curves or as complex polygons, depending on whether their width is relevant.  Features such as regions and lakes can be depicted as polygons. 15 Database System Concepts, 5th Ed. 24.15 ©Silberschatz, Korth and Sudarshan Applications of Geographic Data    Examples of geographic data  map data for vehicle navigation  distribution network information for power, telephones, water supply, and sewage Vehicle navigation systems store information about roads and services for the use of drivers:  Spatial data: e.g, road/restaurant/gas-station coordinates  Non-spatial data: e.g., one-way streets, speed limits, traffic congestion Global Positioning System (GPS) unit - utilizes information broadcast from GPS satellites to find the current location of user with an accuracy of tens of meters.  increasingly used in vehicle navigation systems as well as utility maintenance applications. 16 Database System Concepts, 5th Ed. 24.16 ©Silberschatz, Korth and Sudarshan Spatial Queries  Nearness queries request objects that lie near a specified location.  Nearest neighbor queries, given a point or an object, find the nearest object that satisfies given conditions.  Region queries deal with spatial regions. e.g., ask for objects that lie partially or fully inside a specified region.  Queries that compute intersections or unions of regions.  Spatial join of two spatial relations with the location playing the role of join attribute. 17 Database System Concepts, 5th Ed. 24.17 ©Silberschatz, Korth and Sudarshan Spatial Queries (Cont.)  Spatial data is typically queried using a graphical query language; results are also displayed in a graphical manner.  Graphical interface constitutes the front-end  Extensions of SQL with abstract data types, such as lines, polygons and bit maps, have been proposed to interface with back-end.  allows relational databases to store and retrieve spatial information  Queries can use spatial conditions (e.g. contains or overlaps).  queries can mix spatial and nonspatial conditions 18 Database System Concepts, 5th Ed. 24.18 ©Silberschatz, Korth and Sudarshan Indexing of Spatial Data  k-d tree - early structure used for indexing in multiple dimensions.  Each level of a k-d tree partitions the space into two.  choose one dimension for partitioning at the root level of the tree.  choose another dimensions for partitioning in nodes at the next level and so on, cycling through the dimensions.  In each node, approximately half of the points stored in the sub-tree fall on one side and half on the other.  Partitioning stops when a node has less than a given maximum number of points.  The k-d-B tree extends the k-d tree to allow multiple child nodes for each internal node; well-suited for secondary storage. 19 Database System Concepts, 5th Ed. 24.19 ©Silberschatz, Korth and Sudarshan Division of Space by a k-d Tree  Each line in the figure (other than the outside box) corresponds to a node in the k-d tree   the maximum number of points in a leaf node has been set to 1. The numbering of the lines in the figure indicates the level of the tree at which the corresponding node appears. 20 Database System Concepts, 5th Ed. 24.20 ©Silberschatz, Korth and Sudarshan Division of Space by Quadtrees Quadtrees  Each node of a quadtree is associated with a rectangular region of space; the top node is associated with the entire target space.  Each non-leaf nodes divides its region into four equal sized quadrants   correspondingly each such node has four child nodes corresponding to the four quadrants and so on Leaf nodes have between zero and some fixed maximum number of points (set to 1 in example). 21 Database System Concepts, 5th Ed. 24.21 ©Silberschatz, Korth and Sudarshan Quadtrees (Cont.)  PR quadtree: stores points; space is divided based on regions, rather than on the actual set of points stored.  Region quadtrees store array (raster) information.   A node is a leaf node is all the array values in the region that it covers are the same. Otherwise, it is subdivided further into four children of equal area, and is therefore an internal node.  Each node corresponds to a sub-array of values.  The sub-arrays corresponding to leaves either contain just a single array element, or have multiple array elements, all of which have the same value. Extensions of k-d trees and PR quadtrees have been proposed to index line segments and polygons  Require splitting segments/polygons into pieces at partitioning boundaries  Same segment/polygon may be represented at several leaf nodes 22 Database System Concepts, 5th Ed. 24.22 ©Silberschatz, Korth and Sudarshan R-Trees  R-trees are a N-dimensional extension of B+-trees, useful for indexing sets of rectangles and other polygons.  Supported in many modern database systems, along with variants like R + trees and R*-trees.  Basic idea: generalize the notion of a one-dimensional interval associated with each B+ -tree node to an N-dimensional interval, that is, an N-dimensional rectangle.  Will consider only the two-dimensional case ( N = 2)  generalization for N > 2 is straightforward, although R-trees work well only for relatively small N 23 Database System Concepts, 5th Ed. 24.23 ©Silberschatz, Korth and Sudarshan R Trees (Cont.)   A rectangular bounding box is associated with each tree node.  Bounding box of a leaf node is a minimum sized rectangle that contains all the rectangles/polygons associated with the leaf node.  The bounding box associated with a non-leaf node contains the bounding box associated with all its children.  Bounding box of a node serves as its key in its parent node (if any)  Bounding boxes of children of a node are allowed to overlap A polygon is stored only in one node, and the bounding box of the node must contain the polygon  The storage efficiency or R-trees is better than that of k-d trees or quadtrees since a polygon is stored only once 24 Database System Concepts, 5th Ed. 24.24 ©Silberschatz, Korth and Sudarshan Example R-Tree  A set of rectangles (solid line) and the bounding boxes (dashed line) of the nodes of an R-tree for the rectangles. The Rtree is shown on the right. 25 Database System Concepts, 5th Ed. 24.25 ©Silberschatz, Korth and Sudarshan Search in R-Trees   To find data items (rectangles/polygons) intersecting (overlaps) a given query point/region, do the following, starting from the root node:  If the node is a leaf node, output the data items whose keys intersect the given query point/region.  Else, for each child of the current node whose bounding box overlaps the query point/region, recursively search the child Can be very inefficient in worst case since multiple paths may need to be searched   but works acceptably in practice. Simple extensions of search procedure to handle predicates contained-in and contains 26 Database System Concepts, 5th Ed. 24.26 ©Silberschatz, Korth and Sudarshan Insertion in R-Trees  To insert a data item:  Find a leaf to store it, and add it to the leaf   Handle overflows by splits (as in B+ -trees)    To find leaf, follow a child (if any) whose bounding box contains bounding box of data item, else child whose overlap with data item bounding box is maximum Split procedure is different though (see below) Adjust bounding boxes starting from the leaf upwards Split procedure:  Goal: divide entries of an overfull node into two sets such that the bounding boxes have minimum total area   This is a heuristic. Alternatives like minimum overlap are possible Finding the “best” split is expensive, use heuristics instead  See next slide 27 Database System Concepts, 5th Ed. 24.27 ©Silberschatz, Korth and Sudarshan Splitting an R-Tree Node  Quadratic split divides the entries in a node into two new nodes as follows 1. Find pair of entries with “maximum separation”  2. Place these entries in two new nodes 3. Repeatedly find the entry with “maximum preference” for one of the two new nodes, and assign the entry to that node  4. Preference of an entry to a node is the increase in area of bounding box if the entry is added to the other node Stop when half the entries have been added to one node   that is, the pair such that the bounding box of the two would has the maximum wasted space (area of bounding box – sum of areas of two entries) Then assign remaining entries to the other node Cheaper linear split heuristic works in time linear in number of entries,  Cheaper but generates slightly worse splits. 28 Database System Concepts, 5th Ed. 24.28 ©Silberschatz, Korth and Sudarshan Deleting in R-Trees  Deletion of an entry in an R-tree done much like a B +-tree deletion.  In case of underfull node, borrow entries from a sibling if possible, else merging sibling nodes  Alternative approach removes all entries from the underfull node, deletes the node, then reinserts all entries 29 Database System Concepts, 5th Ed. 24.29 ©Silberschatz, Korth and Sudarshan Multimedia Databases Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use 30 Multimedia Databases  To provide such database functions as indexing and consistency, it is desirable to store multimedia data in a database  rather than storing them outside the database, in a file system  The database must handle large object representation.  Similarity-based retrieval must be provided by special index structures.  Must provide guaranteed steady retrieval rates for continuous-media data. 31 Database System Concepts, 5th Ed. 24.31 ©Silberschatz, Korth and Sudarshan Multimedia Data Formats   Store and transmit multimedia data in compressed form  JPEG and GIF the most widely used formats for image data.  MPEG standard for video data use commonalties among a sequence of frames to achieve a greater degree of compression. MPEG-1 quality comparable to VHS video tape.   MPEG-2 designed for digital broadcast systems and digital video disks; negligible loss of video quality.   stores a minute of 30-frame-per-second video and audio in approximately 12.5 MB Compresses 1 minute of audio-video to approximately 17 MB. Several alternatives of audio encoding  MPEG-1 Layer 3 (MP3), RealAudio, WindowsMedia format, etc. 32 Database System Concepts, 5th Ed. 24.32 ©Silberschatz, Korth and Sudarshan Continuous-Media Data  Most important types are video and audio data.  Characterized by high data volumes and real-time information-delivery requirements.  Data must be delivered sufficiently fast that there are no gaps in the audio or video.  Data must be delivered at a rate that does not cause overflow of system buffers.  Synchronization among distinct data streams must be maintained  video of a person speaking must show lips moving synchronously with the audio 33 Database System Concepts, 5th Ed. 24.33 ©Silberschatz, Korth and Sudarshan Video Servers  Video-on-demand systems deliver video from central video servers, across a network, to terminals  Must guarantee end-to-end delivery rates  Current video-on-demand servers are based on file systems; existing database systems do not meet real-time response requirements.  Multimedia data are stored on several disks (RAID configuration), or on tertiary storage for less frequently accessed data.  Head-end terminals - used to view multimedia data  PCs or TVs attached to a small, inexpensive computer called a set-top box. 34 Database System Concepts, 5th Ed. 24.34 ©Silberschatz, Korth and Sudarshan Similarity-Based Retrieval Examples of similarity based retrieval  Pictorial data: Two pictures or images that are slightly different as represented in the database may be considered the same by a user.   Audio data: Speech-based user interfaces allow the user to give a command or identify a data item by speaking.   E.g., identify similar designs for registering a new trademark. E.g., test user input against stored commands. Handwritten data: Identify a handwritten data item or command stored in the database 35 Database System Concepts, 5th Ed. 24.35 ©Silberschatz, Korth and Sudarshan Mobility Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use 36 Mobile Computing Environments  A mobile computing environment consists of mobile computers, referred to as mobile hosts, and a wired network of computers.  Mobile host may be able to communicate with wired network through a wireless digital communication network  Wireless local-area networks (within a building)   E.g. Avaya’s Orinico Wireless LAN Wide areas networks  Cellular digital packet networks – 3 G and 2.5 G cellular networks 37 Database System Concepts, 5th Ed. 24.37 ©Silberschatz, Korth and Sudarshan Mobile Computing Environments (Cont.)   A model for mobile communication  Mobile hosts communicate to the wired network via computers referred to as mobile support (or base) stations.  Each mobile support station manages those mobile hosts within its cell.  When mobile hosts move between cells, there is a handoff of control from one mobile support station to another. Direct communication, without going through a mobile support station is also possible between nearby mobile hosts  Supported, for e.g., by the Bluetooth standard (up to 10 meters, atup to 721 kbps) 38 Database System Concepts, 5th Ed. 24.38 ©Silberschatz, Korth and Sudarshan Database Issues in Mobile Computing    New issues for query optimization.  Connection time charges and number of bytes transmitted  Energy (battery power) is a scarce resource and its usage must be minimized Mobile user’s locations may be a parameter of the query  GIS queries  Techniques to track locations of large numbers of mobile hosts Broadcast data can enable any number of clients to receive the same data at no extra cost   leads to interesting querying and data caching issues. Users may need to be able to perform database updates even while the mobile computer is disconnected.  e.g., mobile salesman records sale of products on (local copy of) database.  Can result in conflicts detected on reconnection, which may need to be resolved manually. 39 Database System Concepts, 5th Ed. 24.39 ©Silberschatz, Korth and Sudarshan Routing and Query Processing  Must consider these competing costs:  User time.  Communication cost   Connection time - used to assign monetary charges in some cellular systems.  Number of bytes, or packets, transferred - used to compute charges in digital cellular systems  Time-of-day based charges - vary based on peak or off-peak periods Energy - optimize use of battery power by minimizing reception and transmission of data.  Receiving radio signals requires much less energy than transmitting radio signals. 40 Database System Concepts, 5th Ed. 24.40 ©Silberschatz, Korth and Sudarshan Broadcast Data   Mobile support stations can broadcast frequently-requested data  Allows mobile hosts to wait for needed data, rather than having to consume energy transmitting a request  Supports mobile hosts without transmission capability A mobile host may optimize energy costs by determining if a query can be answered using only cached data    Wait for the data to be broadcast  Transmit a request for data and must know when the relevant data will be broadcast. Broadcast data may be transmitted according to a fixed schedule or a changeable schedule.   If not then must either; For changeable schedule: the broadcast schedule must itself be broadcast at a wellknown radio frequency and at well-known time intervals Data reception may be interrupted by noise  Use techniques similar to RAID to transmit redundant data (parity) 41 Database System Concepts, 5th Ed. 24.41 ©Silberschatz, Korth and Sudarshan Disconnectivity and Consistency  A mobile host may remain in operation during periods of disconnection.  Problems created if the user of the mobile host issues queries and updates on data that resides or is cached locally:  Recoverability: Updates entered on a disconnected machine may be lost if the mobile host fails. Since the mobile host represents a single point of failure, stable storage cannot be simulated well.  Consistency : Cached data may become out of date, but the mobile host cannot discover this until it is reconnected. 42 Database System Concepts, 5th Ed. 24.42 ©Silberschatz, Korth and Sudarshan Mobile Updates  Partitioning via disconnection is the normal mode of operation in mobile computing.  For data updated by only one mobile host, simple to propagate update when mobile host reconnects   When data are updated by other computers, invalidation reports inform a reconnected mobile host of out-of-date cache entries   however, mobile host may miss a report. Version-numbering-based schemes guarantee only that if two hosts independently update the same version of a document, the clash will be detected eventually, when the hosts exchange information either directly or through a common host.   in other cases data may become invalid and updates may conflict. More on this shortly Automatic reconciliation of inconsistent copies of data is difficult  Manual intervention may be needed 43 Database System Concepts, 5th Ed. 24.43 ©Silberschatz, Korth and Sudarshan Detecting Inconsistent Updates  Version vector scheme used to detect inconsistent updates to documents at different hosts (sites).  Copies of document d at hosts i and j are inconsistent if 1. the copy of document d at i contains updates performed by host k that have not been propagated to host j (k may be the same as i), and 2. the copy of d at j contains updates performed by host l that have not been propagated to host i (l may be the same as j)  Basic idea: each host i stores, with its copy of each document d, a version vector - a set of version numbers, with an element Vd,i [k] for every other host k  When a host i updates a document d, it increments the version number Vd,i [i] by 1 44 Database System Concepts, 5th Ed. 24.44 ©Silberschatz, Korth and Sudarshan Detecting Inconsistent Updates (Cont.)  When two hosts i and j connect to each other they check if the copies of all documents d that they share are consistent: 1. If the version vectors are the same on both hosts (that is, for each k, Vd,i [k] = Vd,j [k]) then the copies of d are identical. 2. If, for each k, Vd,i [k]  Vd,j [k], and the version vectors are not identical, then the copy of document d at host i is older than the one at host j 3.  That is, the copy of document d at host j was obtained by one or more modifications of the copy of d at host i.  Host i replaces its copy of d, as well as its copy of the version vector for d, with the copies from host j. If there is a pair of hosts k and m such that Vd,i [k]< Vd,j [k], and Vd,i [m] > Vd,j [m], then the copies are inconsistent  Database System Concepts, 5th Ed. That is, two or more updates have been performed on d independently. 24.45 45 ©Silberschatz, Korth and Sudarshan Handling Inconsistent Updates  Dealing with inconsistent updates is hard in general. Manual intervention often required to merge the updates.  Version vector schemes  were developed to deal with failures in a distributed file system, where inconsistencies are rare.  are used to maintain a unified file system between a fixed host and a mobile computer, where updates at the two hosts have to be merged periodically.   are used in database systems where mobile users may need to perform transactions.   Also used for similar purposes in groupware systems. In this case, a “document” may be a single record. Inconsistencies must either be very rare, or fall in special cases that are easy to deal with in most cases 46 Database System Concepts, 5th Ed. 24.46 ©Silberschatz, Korth and Sudarshan End of Chapter Database System Concepts ©Silberschatz, Korth and Sudarshan See www.db-book.com for conditions on re-use 47