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The basic le designs presented here can easily be traced to early developments in data processing References where these les are compared with other techniques are found with Chap 4 Speci c information on le access methods is best obtained from software suppliers, we do not cite their manuals A pile is, of course, the most obvious data collection Most of the pieces of paper that cross our desks are self-describing documents, which can be processed without reference to an external structure description Ledgers and card les are the basis for sequential les; they are often divided into pages or folders with headings There exist corresponding algorithms and structures applicable to memory But when access is into a homogeneous space the access techniques di er, so that the evaluation of the algorithms and their options leads to misleading results when les are being dealt with The bene t of sequential processing is assessed by Charles73 Lomet75 describes the use of tombstones and Leung86 quanti es their e ect in dynamic les Rodriguez-Rosell76 shows that sequential processing remains e ective even on multi-programmed computers and Slonim82 evaluates sequential versus parallel access



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File organizations have been analyzed at several levels of abstraction A pile le is described by Glantz70 and partitioning is exploited by Yue78 A language with a persistent pile le has been implemented by Gray85 Gildersleeve71 and Grosshans86 address programming for sequential les Severance72 evaluates sequential les and buckets, and the di erence between fetch and get-next operations Shneiderman76 and Palvia85 analyze batch fetching in sequential les Willard86 presents algorithms for direct update of sequential les and Nevaleinen77 has rules for their blocking The problem of merging cosequential les has often been analyzed, a recent example is Levy82 In Shneiderman78 a form of binary search is developed, Li87 extends it to batched operations Ghosh69 proposes to use probing instead of lower-level indexes Concurrent access of direct primary and over ow areas is proposed by Groner74 File systems which implement various methods are described by Judd73 Martin75 describes many le operations in detail and provides design guidance Indexing In o ces, large sequential les in drawers are partitioned using labeled folders Labels on the drawers provide a second level of partitioning of these les to accelerate access Multiple indexes, based on title, authors, and subject, can be found in any library Chapin68 includes the programming issues of indexed-sequential les Further information is available in the literature of software suppliers (see Appendix B) Seaman66 and Lum73 treat indexed-sequential les analytically Allen68 describes an application using large indexed-sequential les Indexed-sequential les were considered in Brown72 to comprise such a signi cant fraction of disk usage that the ibm 3330 disk system design was greatly in uenced by their behavior The construction of index trees in memory has been frequently discussed in the computer science literature It is important to realize when using these references that the index trees found in storage les have very high fanout ratios and do not contain the data within themselves Trees containing data are discussed in Chap 9-2 Landauer63 considers dense, dynamically changing indexes Bose69 , Welch in King75 , and Larson81 evaluate access to indexed les using formal and mathematical models Maio84 evaluates block anchors versus record anchors The B-tree algorithm was presented and evaluated by Bayer72O,S It is further analyzed by Horowitz78 ; Comer79 provides an excellent summary, and Knuth73S includes some le-oriented considerations Ghosh69 combines B-trees with probing A review by March83 considers partial indexes Yao78 analyzed the density of B-trees under random insertions and deletions Held78 and Aho79U evalute B-trees in relational databases Indexes are used for bibliographic retrieval in Dimsdale73 Guttman84 extends B-trees for spatial data An early system using multiple indexes, tdms, is described by Bleier68 ; Bobrow in Rustin72 develops this approach Multiattribute access via indexes is evaluated by Lum70 , Mullin71 , and Schkolnick in Kerr75 O Connell71 describes use of multiple indexes Selection, and its optimization, among multiple indexes is considered by many authors, among them Lum71L , Stonebraker74 , Shneiderman74,77 , Schkolnick75 Hammer76 , Yao77 , Anderson77 , Comer78 , Hammer79 , Yamamoto79 , Rosenberg81 , Saxton83 , as well as Whang85 The work is limited by assumptions on the distribution of the index value distributions for the queries Piatetsky84 suggests modeling the distribution by a stepwise function Indexes are used for partial matching by Lum70 Strategies of combinatorial index organization were developed by Ray-Chaudhuri68 , Bose69 , Shneiderman77 , and Chang81 Procedures for estimating selectivity in a le are surveyed by Christodoulakis83 , Flajolet85 and Astrahan87 do it in O(n), Chu82 uses statistical models Federowicz87 nds Zip an distributions.

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