J.R. Driscoll, N. Sarnak, D.D. Sleator, R.E. Tarjan, Making data structures persistent, Journal of Computer and System Sciences 38 (1989) 86--124.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....update operations, finger search trees, data structures, complexity 1 Introduction The balanced search tree is one of the most common data structures used in algorithms. Assuming that the update position is known, balanced search trees with O(1) amortized update time have been presented long ago ([6, 14]) It has also been known ( 6, 16] that updates can be performed in O(1) structural changes, but the nodes to be changed have to be searched in#13 n) time. Levcopoulos and Overmars ( 13] presented an algorithm achieving O(1) worst case update time by using a global splitting lemma that is based ....

....data structures, complexity 1 Introduction The balanced search tree is one of the most common data structures used in algorithms. Assuming that the update position is known, balanced search trees with O(1) amortized update time have been presented long ago ( 6, 14] It has also been known ([6, 16]) that updates can be performed in O(1) structural changes, but the nodes to be changed have to be searched in#13 n) time. Levcopoulos and Overmars ( 13] presented an algorithm achieving O(1) worst case update time by using a global splitting lemma that is based on a pebble game combined with the ....

J.R. Driscoll, N. Sarnak, D.D.Sleator and R.E. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, 38:86-124, 1989.

....A i # ,j # arrays altogether and only ever requires finding the median in two augmented binary search trees. The cost of this simplification is only an O(log log n) factor in the space requirement. Although there are persistent binary search trees that require only O(n) space for n operations [4, 10], these trees are not augmented and thus do not work in our application. In particular, they do not allow us to make use of Lemma 2. 8 4.3 A Constant Query Time Subquadratic Space Data Structure Next we sketch a range median query data structure with constant query time and subquadratic space. ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86--124, February 1989.

.... approach Roughly speaking, we use persistence as follows: To solve a given generalized problem we rst identify a di erent, but simpler, generalized problem and devise a data structure for it that also supports updates (usually just insertions) We then make this structure partially persistent [14] and query this persistent structure appropriately to solve the original problem. We illustrate this approach for the generalized 3 dimensional range searching problem, where we are required to preprocess a set, S, of n colored points in R so that for any query box q = a; b] c; d] e; f ....

....colors of the ones in [a; b] c; d] are reported. These are precisely the distinct colors of the points contained in [a; b] c; d] e; 1) The query time follows from Lemma 1.3. To analyze the space requirement, we note that the structure of Lemma 1. 3 satis es the conditions given in [14]. Speci cally, it is a pointer based structure, where each node is pointed to by only O(1) other nodes. As shown in [14] any modi cation made by a persistent update operation on such a structure adds only O(1) amortized space to the resulting persistent structure. By Lemma 1.3, the total time ....

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38:86-124, 1989.

....Maude 1.0, is implemented as a hierarchy of C class libraries. There are a large number of incremental improvements, but we highlight the major ones. The Core Rewrite Engine. The most radical change from Maude 1. 0 is the use of a novel term representation based on persistent data structures [9] for E rewriting [11] In some cases, new rewriting algorithms based on this representation can dramatically improve the rewriting speed for large terms. Table 1 compares the performance of Maude with and without this representation for the example in Appendix A on a 2.8GHz, 2GByte Intel Xeon. The ....

J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan. Making data structures persistent. Journal of Computer and System Science, 38:86--124, 1989.

....operations, nger search trees, data structures, complexity 1. INTRODUCTION The balanced search tree is one of the most common data structures used in algorithms. Assuming that the update position is known, balanced search trees with O(1) amortized update time have been presented long ago ([6, 14]) It has also been known ( 6, 16] that updates can be performed in Research conducted while visiting Computer Technology Institute (CTI) and University of Patras, Greece. Basic Research in Computer Science, www.brics.dk, funded by the Danish National Research Foundation. Permission to make ....

....data structures, complexity 1. INTRODUCTION The balanced search tree is one of the most common data structures used in algorithms. Assuming that the update position is known, balanced search trees with O(1) amortized update time have been presented long ago ( 6, 14] It has also been known ([6, 16]) that updates can be performed in Research conducted while visiting Computer Technology Institute (CTI) and University of Patras, Greece. Basic Research in Computer Science, www.brics.dk, funded by the Danish National Research Foundation. Permission to make digital or hard copies of all or ....

J.R. Driscoll, N. Sarnak, D.D.Sleator and R.E. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, 38:86-124, 1989.

....aggregation data structure R x for a data set with y points. This is not a bad result, but there is a high amount of redundancy because the instances of R g for consecutive TT dimension coordinates differ by only a few data points. To remove this redundancy, we use multiversion data structures [9]. More precisely, instead of having a separate instance of R g for each data point s PSum g , we encode all R g instances of the PSum g of all data points in a single multiversion variant of R g . We do the same for EPSum g . This somewhat restricts the choice of R g (to those with existing ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences (JCSS), 38(1):86--124, 1989.

....internal memory balanced search tree. It uses linear space, O(N B) blocks, to store N elements; supports updates in O(log B N) I Os; and performs one dimensional range queries in optimal O(log B N T B) I Os, where T is the number of reported elements. Using a general technique by Driscoll et al. [14], persistent versions of the B tree have also been developed [11, 26] A persistent data structure maintains a history of all updates performed on it, such that queries can be answered on any of the previous versions of the structure, while updates can only be performed on the most recent version ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38:86-- 124, 1989.

....internal memory balanced search tree. It uses linear space, O(N=B) blocks, to store N elements, supports updates in O(log B N) I Os, and performs one dimensional range queries in optimal O(log B N T=B) I Os, where T is the number of reported elements. Using a general technique by Driscoll et al. [14], persistent versions of the B tree have also been developed [11; 26] A persistent data structure maintains a history of all updates performed on it, such that queries can be answered on any of the previous versions of the structure, while updates can be performed on the most recent version (thus ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38:86-124, 1989.

....polynomial functions and return a value x that maximizes or minimizes the sum of all the functions evaluated at x. One could imagine using such a data structure with plane sweep to perform operations like maintaining the minimum in sums of functions, or combining it with the persistence paradigm [7] to get an efficient representation of sums of functions. Open Problem 1. Find additional applications for our interval data structure. Another obvious open problem is that of improving the running time of the algorithm for Problem 6. In particular, it would be nice to produce an algorithm which ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86- 124, February 1989.

.... Over the last fifteen years, there has been considerable development of persistent data structures, those in which not only the current version, but also older ones, are available for access (partial persistence) or updating (full persistence) In particular, Driscoll, Sarnak, Sleator, and Tarjan [5] developed efficient general methods to make pointer based data structures partially or fully persistent, and Dietz [3] developed an efficient general method to make array based structures fully persistent. These general methods support updates that apply to a single version of a structure at a ....

....of L. return a new list formed by catehating L and R, with L first. We seek implementations of these operations (or specific subsets of them) on persistent lists: any operation is allowed on any previously constructed list or lists at any time. For discussions of various forms of persistence see [5]. A stack is a list on which only PUSH and POP are allowed. A queue is a list on which only INJECT and POP are allowed. A steque (stack ended queue) is a list on which only PUSH, POP, and INJECT are allowed. Finally, a deque (double ended queue) is a list on which all four operations PUSH, POP, ....

J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan. Making data structures persistent. J. of Computer and System Science, 38:86-124, 1989.

....Data Structures Con uently Persistent Amos Fiat Haim Kaplan Reality is merely an illusion, albeit a very persistent one. Albert Einstein (1875 1955) Abstract We address a longstanding open problem of [10, 9], and present a general transformation that transforms any pointer based data structure to be con uently persistent. Such transformations for fully persistent data structures are given in [10] greatly improving the performance compared to the naive scheme of simply copying the inputs. Unlike ....

....persistent one. Albert Einstein (1875 1955) Abstract We address a longstanding open problem of [10, 9] and present a general transformation that transforms any pointer based data structure to be con uently persistent. Such transformations for fully persistent data structures are given in [10] , greatly improving the performance compared to the naive scheme of simply copying the inputs. Unlike fully persistent data structures, where both the naive scheme and the fully persistent scheme of [10] are feasible, we show that the naive scheme for con uently persistent data structures is ....

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J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan. Making data structures persistent. J. of Computer and System Science, 38:86-124, 1989.

....method for externalizing plane sweep algorithms; persistezt 13 trees: an off line method for constructing an optimal space persistent version of the B tree data structure. For hatched problems this gives a factor of t3 improvement over the generic persis tence techniques of Driscoll et al. [11]; bateh fi ltermg: a general method for performing K simultaneous external memory searches in data structures that can be modeled as planar layered dags and in certain fractional cascaded data struc tures; oralroe filtering: A technique based on the work of Tamassia and Vitter [35] that ....

....dictionary in external memory. In some cases, however, it may be advantageous to be able to access previous versions of the data structure. Being able to access such previous versions is known as pcrsistccc, and there exist very general techniques for making most data structures persistent [11]. Persistence can be implemented either in an on line fashion (i.e. where the tree updates are coming on line) or in an off line fashion (i.e. where one is given the sequence of tree updates in advance) For the on line case, the method of Driscoll et al. Lspace [11] can be applied to ....

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Trojan, "Making Data Structures Persistent," J. Cornput. System Sci. 38 (1989), 86 124.

....dictionary in external memory. In some cases, however, it may be advantageous to be able to access previous versions of the data structure. Being able to access such previous versions is known as persistence, and there exist very general techniques for making most data structures persistent [11]. Persistence can be implemented either in an on line fashion (i.e. where the tree updates are coming on line) or in an off line fashion (i.e. where one is given the sequence of tree updates in advance) For the on line case, the method of Driscoll el al, 11] can be applied to hysterical ....

.... most data structures persistent [11] Persistence can be implemented either in an on line fashion (i.e. where the tree updates are coming on line) or in an off line fashion (i.e. where one is given the sequence of tree updates in advance) For the on line case, the method of Driscoll el al, [11] can be applied to hysterical B trees as described by Maier and Salveter [26] Since it is on line, this structure requires O(N log , I Os to construct, which is optimal in an on line setting. Unfortunately, this is a factor of B away from optimal for the sort of batch geometric problems we would ....

J. R. Driscoll, N. Sarnak, D. D. Sleator & R. E. Tar- jan, "Making data structures persistent," J. Coalput. System Sci. 38 (1989), 86 124.

....and therefore should be useful in practice. 1.2.2 Persistence A data structure normally provides two types of operations to a subset of a universe: queries, which return information without modifying the subset, and updates, which may change the subset. In the terminology of Driscoll et al. DSST89] an abstract data structure is ephemeral if an update destroys the version of the concrete data structure being changed; i.e. there is always one and only one valid version. As described in Section 1.1.1, destructive list operations affect ephemeral lists. A data structure that allows queries ....

.... has been much previous work on making various data structures partially or fully persistent [CG93, Cha85, Col86, DM85, HM81, Mye82, Mye83, Mye84, Ove81a, RTD83, ST86] There have also been some results providing general methods for making entire classes of data structures persistent [Die89, DR91, DSST89, Ove81b, Sar86] In particular, Driscoll et al. DSST89] provide a method by which many pointer based data structures may be made partially or fully persistent with only a constant factor overhead in time (over the ephemeral version) and using only a constant amount of space per persistent ....

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86-- 124, February 1989.

....polynomial functions and return a value x that maximizes or minimizes the sum of all the functions evaluated at x. One could imagine using such a data structure with plane sweep to perform operations like maintaining the minimum in sums of functions, or combining it with the persistence paradigm [8] to get an ecient representation of sums of functions. An obvious open problem is that of improving the running time of the algorithm for Problem 6. In particular, it would be nice to produce an algorithm which is output sensitive to the size of the k occupied set. Open Problem 1. Find an output ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86{ 124, February 1989.

....Figure 2, nodes appearing in many versions are stored only once in the archive. If a node occurs in version i, then the timestamp of the corresponding node in the archive contain i. We use time intervals to describe the sequence of versions for which a node exists. For example, the time intervals [1 3,5,7 9] denotes the set 1,2,3,5,7,9. A node that does not have a timestamp is assumed to inherit the timestamp of its parent. For example, the name node under the emp (t= 2 3] inherits the timestamp t= 2 3] Observe that it is a property of the archive that the timestamp of a node is always a superset ....

....is smaller than the accumulated past versions. Here we capitalize on the fact that the time sequences associated with elements can be compactly represented as a small number of intervals and are often inherited from a parent element. Contributions. We extend the technique of Driscoll et al. [9] (See Section 6) and develop an archiving tool for XML data, which compacts a sequence of versions into a single XML document. We show that our approach is viable and comes with several bene ts: We show that the compacted archive can be constructed eciently, i.e. a new version can be e ....

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J. R. Driscoll and N. Sarnak and D. D. Sleator and R. E. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, 38(1):86-124, 1989.

....have the same hashed value) and obtain an efficient realization of the memory structure for history sensitive heuristics. 3. Persistent dynamic sets to support heuristics The above desired time and space requirements can in fact be obtained through the use of persistent dynamic sets, see [17, 8] and references therein. Ordinary data structures are ephemeral [8] because when a change is executed the previous version is destroyed. Now, in many contexts like computational geometry, editing, implementation of very high level programming languages, and, last but not least, the context of ....

....of the memory structure for history sensitive heuristics. 3. Persistent dynamic sets to support heuristics The above desired time and space requirements can in fact be obtained through the use of persistent dynamic sets, see [17, 8] and references therein. Ordinary data structures are ephemeral [8] because when a change is executed the previous version is destroyed. Now, in many contexts like computational geometry, editing, implementation of very high level programming languages, and, last but not least, the context of history sensitive heuristics considered in this paper, multiple ....

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan, Making data structures persistent, in: Proc. 18th Ann. ACM Symp. on Theory of Computing, Berkeley, CA (1986) 109--121.

....is a subset of C , i.e. l(v) C . A labeling l : V (T ) 2 C of a tree T is a set of labelings for the nodes in T . In the rest of the paper all trees are rooted trees. DEFINITION 2 (Persistent data structures) The concept of persistent data structures was introduced by Driscoll et al. in [13]. A data structure is partially persistent if all previous versions remain available for queries but only the newest version can be modified. A data structure is fully persistent if it allows both queries and updates of previous versions. An update may operate only on a single version at a time, ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. J. Computer Systems Sci., 38(1):86--124, 1989.

....worst case creates a logarithmic storage overhead and requires more elaborate query processing. In contrast, we propose to use a different approach in indexing animated objects which combines a spatial index (R Tree) with the partially persistent methodology. A data structure is called persistent [14] if an update applied to it creates a new version of the data structure while the previous version is still retained and can be accessed. A data structure that does not keep its past is called ephemeral. Partial persistence implies that all versions can be accessed but only the newest version can ....

J. Driscoll, N. Sarnak, D. Sleator and R.E. Tarjan. Making Data Structures Persistent. In Proc. of the Eighteenth Annual ACM Symposium on Theory of Computing, Berkeley, California, 1986.

....changes is applied to the structure) There is access ability for any of these versions. Update ability is limited to the latest version, in case we have partial persistence, or exists for all versions, in case we have full persistence. This idea, along with general techniques, has been studied in [6]. We may view an overlapped family of Quadtrees, along with the Quadtree of the latest image not yet inserted in the family, as a form of a persistent Quadtree, where there is access to any older version of the tree and update ability to its latest version. When a new tree (a new version) appears, ....

....Quadtree, where there is access to any older version of the tree and update ability to its latest version. When a new tree (a new version) appears, this latest version is inserted in the family and may no longer be updated. The difference between this form of persistence and the one described in [6] is that at the expense of some space overhead the access times remain unaffected. The family method could be characterized as partial persistence with time benefit. 4 j j j Phi Phi Phi Phi H H H H Gamma Gamma Gamma Gamma Gamma j Theta Theta L LL Gamma Gamma ....

Driscoll, J R, Sarnak, N, Sleator, D D and Tarjan, R E `Making Data structures persistent' Journal of Computer System Sciences Vol 38 No 1 (1989) pp 86-124

....for both processes are copied before being modi ed. The graphical representation of the process that makes the shared structure progressively disjoined is remarkably similar to cell division by mitosis; hence the name of the project. We call a structure shared in a COW way a persistent structure [1], because several versions (one for each process) of the same structure are kept at a time. There must be only one access point to each version of the structure and items within the structure must be reference counted. Each time an item is modi ed, the path that leads from the entry point to the ....

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. In Proceedings of the Eighteenth Annual ACM Symposium on Theory of Computing, pages 109-121, Berkeley, California, 28-30 May 1986. 5

.... approach is to exploit the monotonicity of the temporal dimension, and transform a 2 dimensional spatial access method to become partially persistent [38, 21, 14, 16, 32] A partially persistent structure logically stores all its past states and allows updates only to its most current state [9, 20, 4, 39, 18, 29]. A historical query about time t is directed to the state the structure had at time t. Hence, answering such a query is proportional to the number of alive objects the structure contains at time t. That is, it behaves as if an ephemeral structure was present for time t, indexing the alive ....

....is proportional to the number of alive objects the structure contains at time t. That is, it behaves as if an ephemeral structure was present for time t, indexing the alive objects at t. Two ways have been proposed to achieve partial persistence: the overlapping [6] and multi version approaches [9]. In the overlapping approach [21, 38] a 2 dimensional index is 2 conceptually maintained for each time instant. Since consecutive trees do not differ much, common (overlapping) branches are shared between the trees. While easy to implement, overlapping creates a logarithmic overhead on the ....

J. Driscoll, N. Sarnak, D. Sleator, and R.E. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, Vol. 38, No. 1, pages 86--124, 1989.

....representation corresponding to the Voronoi diagram, does not change. Let the set T = f 0 1 ; 0 l g, l = O(T (n) denote now the thus rened set of times. Since the successive Voronoi diagrams are monotone subdivisions, a persistent data structure can be used for point location [4, 6]. Given the list of times and the corresponding changes at these times in the monotone subdivisions, the data structure can be constructed in time O(T (n) log n) and its size is O(T (n) log n) Applying this to the Moving Voronoi query q = s q ; t q ) we rst perform an O(log n) binary search on ....

.... = O(N log n) where N = O(T (n) 2 ) During the sweep we can build a point location structure for M as described, e.g. by Sarnak and Tarjan [13] or Cole [2] This point location data structure has space complexity O(T (n) 2 ) and a point location query takes time O(log(T (n) O(log n) [13, 4]. Assume we are given a Moving Voronoi query q = s q ; t q ) We rst locate the region in M that contains the point s q . Next we have to locate t q in the stabbing sequence corresponding to this region. We use binary search trees to store the stabbing sequences. Since the stabbing sequences ....

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. J. Comput. Syst. Sci., 38:86124, 1989.

....that every structure created is automatically fully persistent (accessible and modifiable) Persistent data structures arise not only in functional programming but also in text, program, and file editing and maintenance; computational geometry; and other algorithmic application areas. See [6, 9, 11, 10, 13, 12, 14, 15, 18, 24, 25, 26, 27, 28, 29, 30]. Several papers have dealt with the problem of adding persistence to general data structures in a way that is more efficient than the obvious solution of copying the entire structure whenever a change is made. In particular, Driscoll, Sarnak, Sleator, and Tarjan [13] described how to make ....

....18, 24, 25, 26, 27, 28, 29, 30] Several papers have dealt with the problem of adding persistence to general data structures in a way that is more efficient than the obvious solution of copying the entire structure whenever a change is made. In particular, Driscoll, Sarnak, Sleator, and Tarjan [13] described how to make pointer based structures fully persistent using a technique called node splitting, which is related to fractional cascading [7] in a way that is not yet fully understood. Dietz [10] described a method for making array based structures persistent. Additional references on ....

[Article contains additional citation context not shown here]

J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan. Making data structures persistent. J. of Computer and System Science, 38:86--124, 1989.

....side effects, has the property that every structure created is automatically fully persistent. Persistent data structures arise not only in functional programming but also in text, program, and file editing and maintenance; computational geometry; and other algorithmic application areas. See [6, 10, 11, 12, 13, 14, 15, 16, 24, 37, 38, 39, 40, 41]. A number of papers have discussed ways of making specific data structures, such as search trees, persistent. A smaller number have proposed methods for adding persistence to general data structures without incurring the huge time and space costs of the obvious method, which is to copy the ....

....trees, persistent. A smaller number have proposed methods for adding persistence to general data structures without incurring the huge time and space costs of the obvious method, which is to copy the entire structure whenever a change is made. In particular, Driscoll, Sarnak, Sleator, and Tarjan [14] described how to make pointer based structures persistent using a technique called node splitting, which is related to fractional cascading [7] in a way that is not yet fully understood. Dietz [11] described a method for making array based structures persistent. Additional references on ....

[Article contains additional citation context not shown here]

J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan. Making data structures persistent. J. of Computer and System Sciences, 38:86--124, 1989.

.... Over the last fteen years, there has been considerable development of persistent data structures, those in which not only the current version, but also older ones, are available for access (partial persistence) or updating (full persistence) In particular, Driscoll, Sarnak, Sleator, and Tarjan [5] developed ecient general methods to make pointer based data structures partially or fully persistent, and Dietz [3] developed an ecient general method to make array based structures fully persistent. These general methods support updates that apply to a single version of a structure at a time, ....

....R) return a new list formed by catenating L and R, with L rst. We seek implementations of these operations (or speci c subsets of them) on persistent lists: any operation is allowed on any previously constructed list or lists at any time. For discussions of various forms of persistence see [5]. A stack is a list on which only push and pop are allowed. A queue is a list on which only inject and pop are allowed. A steque (stack ended queue) is a list on which only push, pop, and inject are allowed. Finally, a deque (double ended queue) is a list on which all four operations push, pop, ....

J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan, Making data structures persistent, J. of Computer and System Science, 38 (1989), pp. 86-124.

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J.R. Driscoll, N. Sarnak, D.D. Sleator, R.E. Tarjan, Making data structures persistent, Journal of Computer and System Sciences 38 (1989) 86--124.

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Driscoll, J R, Sarnak, N, Sleator, D D and Tarjan, R E `Making Data structures persistent' Journal of Computer System Sciences Vol 38 No 1 (1989) pp 86-124

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Driscoll, J. R., Sarnak, N., Sleator, D. D., and Tarjan, R. E. 1989. Making Data Structures Persistent. Journal of Computer and System Sciences 38, 1, 86--124.

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86--124, 1989.

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. J. Comput. Syst. Sci., 38(1):86--124, Feb. 1989.

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Driscoll, James R., Sarnak, Neil, Sleator, Daniel D., & Tarjan, Robert E. (1989). Making data structures persistent. Journal of computer and system sciences, 38(1), 86-124.

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J. Driscoll, N. Sarnak, D. D. Sleator, and R. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, 38:86--124, 1989.

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J.R. Driscoll, N. Sarnak, D.D.Sleator and R.E. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, 38:86-124, 1989.

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J. R. Driscoll, N. Sarnak, D. D. Sleator, R. E. Tarjan, Making data structures persistent, J. Comp. Syst. Sci. 38 (1989) pp. 86-124.

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J. R. Driscoll et al. Making Data Structures Persistent. Journal of Computer and System Sciences, 38(1):86--124 (1989).

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J.R. Driscoll, N. Sarnak, D. Sleator and R.E. Tarjan, Making Data Structures Persistent, Journal of Comp. and Syst. Sciences, Vol 38, pp 86-124, 1989.

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86--124, February 1989.

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86--124, Feb. 1989.

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J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86--124, February 1989.

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J. Driscoll, N. Sarnak, D. Sleator, and R.E. Tarjan. Making data structures persistent. In Proc. of the ACM Symposium on Theory of Computing, 1986.

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J. Driscoll, N. Sarnak, D. Sleator, and R.E. Tarjan. Making Data Structures Persistent. Journal of Computer and System Sciences, Vol. 38, No. 1, pages 86--124, 1989.

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J. R. Driscoll, N. Sarnak, D. Sleator, and R. Tarjan. Making data structures persistent. J. of Computer and System Science, 38:86-124, 1989.

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J.R. Driscoll, N. Sarnak, D.D. Sleator, R.E. Tarjan, Making data structures persistent, Journal of Computer and System Sciences 38 (1989) 86--124.

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J.R.Driscoll, N. Sarnak, D.D. Sleator, and R.E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86-124, 1989.

No context found.

J. R. DRISCOLL, N. SARNAK, D. D. SLEATOR AND R. E. TARJAN. Making data structures persistent. J. Comput. Sys. Sci. 38 (1989), 86-- 124.

No context found.

J. R. Driscoll, N. Sarnak, D. D. Sleator, and R. E. Tarjan. Making data structures persistent. J. Comput. Syst. Sci., 38(1):86--124, Feb. 1989.

No context found.

J. Driscoll, N. Sarnak, D. Sleator and R.E. Tarjan. Making Data Structures Persistent. In Proc. of the Eighteenth Annual ACM Symposium on Theory of Computing, Berkeley, California, 1986.

No context found.

J.R.Driscoll, N. Sarnak, D.D. Sleator, and R.E. Tarjan. Making data structures persistent. Journal of Computer and System Sciences, 38(1):86-124, 1989.

No context found.

J.R. Driscoll, N. Sarnak, D. Sleator and R.E. Tarjan, Making Data Structures Persistent, Journal of Comp. and Syst. Sciences, Vol 38, pp 86-124, 1989.

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