P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for data models with constraints and classes. In Proc. ACM Symp. on Principles of Database Sys., pages 233--243, 1993.  Home/Search   Document Details and Download   Summary   Related Articles   Check

....of the query rectangle lies on the boundary of the data space. Arge et al. ASV99] design the external priority search tree that answers such queries optimally (i.e. logarithmic query cost and linear space consumption) Earlier, non optimal structures for RS and 3 sided queries can be found in [IKO87, KRVV96, RS94, SR95]. The first study on RS queries for moving objects [KGT99a] deals with only 1D data. Agarwal et al. AAE00] present several interesting results in the 2D space following the kinetic approach [BGH97] In particular, they show that if queries arrive in chronological order, a RS can be answered with ....

Kanellakis, P., Ramaswamy, S., Vengroff, D., Vitter, J. Indexing for Data Models with Constraints and Classes. Journal of Computer and System Sciences, 52(3): 589-612, 1996.

....the I O interval tree [6] used in [18] and the binary blocked I O interval tree (the BBIO tree for short) developed and used in [20] for out of core isosurface extraction. This scan and distribute paradigm enables them to perform preprocessing to build these trees (as well as the metablock tree [54]) in an I O optimal way; see Chiang and Silva [19] for a complete review of these data structures and techniques. 2.2.2 Out of Core Pointer De Referencing Typical input datasets in scientific visualization and computer graphics are given in compact indexed forms. For example, scalar field ....

....up to some constant factor. Therefore the overall space complexity is optimal O(N B) disk blocks. As we shall see in Sec. 2.3.1. 3, the above data structure supports queries in non optimal O(log 2 K B) I O s (where K is the number of intervals reported) and we can use the corner structures [54] to achieve optimal O(log B N K B) I O s while keeping the space complexity optimal. 2.3.1.3 Query Algorithm The query algorithm for the BBIO tree is very simple and mimics the query algorithm for the binary interval tree T . Given a query point q, we perform the following recursive process ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for Data Models with Constraints and Classes. Journal of Computer and System Sciences, 52(3):589--612, 1996.

....range queries which request all points in a d dimensional rectangle. This also models multi attribute queries. In addition, answers to multidimensional range queries are important for supporting constraint query languages and queries on class hierarchies in object oriented databases [16]. Answers to 2D range and 3 sided queries (rectangles with one direction going to in nity) are the most important special cases to handle. For range queries over N points with answers of size T , it is easy to use Search DAGs to create an authenticated version of Willard s data structure [26] ....

P. Kanellakis, S. Ramaswamy, D. Vengro, and J. Vitter. Indexing for data models with constraints and classes. Journal of Computer and System Sciences, 52(3):589-612, 1996.

....range queries which request all points in a d dimensional rectangle. This also models multi attribute queries. In addition, answers to multi dimensional range queries are important for supporting constraint query languages and queries on class hierarchies in object oriented databases [12]. Answers to 2D range and 3 sided queries (rectangles with one direction going to in nity) are the most important special cases to handle. For range queries over N points with answers of size T , we use Search DAGs to answer d dimensional range queries with a VO of size and construction time ....

P. Kanellakis, S. Ramaswamy, D. Vengro, and J. Vitter. Indexing for Data Models with Constraints and Classes. Journal of Computer and System Sciences, 52(3), pp. 589-612, 1996.

....case) We can detect these cases and remove unnecessary duplicate elimination steps automatically (this is also the reason for having the se I ect clause to eliminate duplicates by default) uuery Processing Efficient execution of queries on the chosen encoding. General constraint encoding [Kanellakis et al. 1993]. Interval encoding: equality on time instants maps into interval intersection = indices for intervals set operations on points map to = normalization followed by the original operation = on the fly multidimensional indices Intervals (or hypercubes) are important in general: more ....

Kanellakis, R, Ramaswamy, S., Vengroff, D., and Vitter, J. (1993). Indexing for Data Models with Constraints and Classes. In ACM Symposium on Principles of Database Systems.

....the desirable static query I O time of O(log B n t=B) on average inputs) is heuristic and usually validated through experimentation. Moreover, their worst case performance is much worse than the optimal bounds achievable for dynamic external 1 dimensional range searching using B trees(see [KRV] for a more complete reference on the field) In this paper, we are interested in obtaining algorithms with good worst case performance. Using path caching, we study tradeoffs between time and space in secondary memory. Two special cases of 2 dimensional range searching have been studied ....

....between time and space in secondary memory. Two special cases of 2 dimensional range searching have been studied extensively in the literature. The first one is dynamic interval management in secondary storage. This problem is crucial to indexing in constraint databases and temporal databases [KKR, KRV]. It is shown in [KRV] that the key component of dynamic interval management is answering stabbing queries. Given a set of input intervals, to answer a stabbing query for a point q we have to report all intervals that intersect q. Elegant solutions exist for this problem in main memory. The ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter, "Indexing for Data Models with Constraints and Classes," Proc. 12th ACM PODS (1993), 233--243, (A complete version of the paper appears in Technical Report 93-21, Brown University.).

.... representations, one finds contributions on the expressiveness of the various representation formalisms [2, 5, 4] on the complexity of query evaluation [9, 12, 25, 31] and on data structures and algorithms to be used in the representation of constraint expressions and in query evaluation [28, 7, 8, 22]. However, much less has been done on extending other parts of traditional database theory, for instance schema design and dependency theory. It should be clear that dependency theory is of interest in this context. For instance, in [18] one finds a taxonomy of dependencies that are useful for ....

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for data models with constraints and classes. In Twelfth ACM Symposium on Principles of Database Systems, pages 233--243, Washington, DC, May 1993.

....requires the handling of multidimensional range queries of point data. A large number of external data structures and algorithms have been proposed to deal with this problem. In contrast to the two dimensional case where solutions that provide provable good performance exist (see for example [20, 16, 6]) most data structures for high dimensional data are aimed at achieving good practical performance (A recent survey can be found in [12] Among them, the R tree [13] has been widely accepted as an efficient external tree structure for handling multidimensional data sets. Many dynamic R tree ....

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for data models with constraints and classes. Journal of Computer and System Science, 52(3):589-- 612, 1996.

....as expression trees in Constraint Query Algebra [GK96] UMB CDB is designed as an experimental platform. Our approach of building a CDB around an algebra based middle layer is meant to provide a foundation for testing various implementational strategies (e.g. indexing structures such as [KRVV93]) and query optimization strategies for linear constraint systems. The eventual goal is a system that combines the versatility and expressiveness of constraints with the commercial success of the relational approach. 1 Supported by NSF Grant #IRI 9733678. a) b) Figure 1: Database schema ....

P. C. Kanellakis, S. Ramaswamy, D. E. Vengro, J. S. Vitter. Indexing for Data Models with Constraints and Classes. JCSS, 52(3): 589-612, 1996

....Department of Computer Science, University of Aarhus, Denmark and I.N.R.I.A. Sophia Antipolis, France. Email: jsv cs.duke.edu. 1 Introduction There has recently been much effort toward developing worst case I O efficient external memory data structures for range searching in two dimensions [1, 2, 4, 8, 12, 13, 20, 26, 28, 29]. In their pioneering work, Kanellakis et al. 13] showed that the problem of indexing in new data models (such as constraint, temporal, and object models) can be reduced to special cases of twodimensional indexing. Refer to Figure 1) In particular they identified the 3 sided range searching ....

....Sophia Antipolis, France. Email: jsv cs.duke.edu. 1 Introduction There has recently been much effort toward developing worst case I O efficient external memory data structures for range searching in two dimensions [1, 2, 4, 8, 12, 13, 20, 26, 28, 29] In their pioneering work, Kanellakis et al. [13] showed that the problem of indexing in new data models (such as constraint, temporal, and object models) can be reduced to special cases of twodimensional indexing. Refer to Figure 1) In particular they identified the 3 sided range searching problem (Figure 1(c) to be of major importance. In ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for data models with constraints and classes. Journal of Computer and System Sciences, 52(3):589--612, 1996.

....and by a Sloan Fellowship. Indexing Moving Points 1 1 Introduction Efficient indexing schemes that support range searching and its variants are central to any large database system. In relational database systems and SQL, for example, one dimensional range searching is a commonly used operation [22, 36]. Various two dimensional range searching problems are crucial for the support of new language features, such as constraint query languages [22] and class hierarchies in object oriented databases [22] In spatial databases such as geographic information systems (GIS) range searching obviously ....

....to any large database system. In relational database systems and SQL, for example, one dimensional range searching is a commonly used operation [22, 36] Various two dimensional range searching problems are crucial for the support of new language features, such as constraint query languages [22] and class hierarchies in object oriented databases [22] In spatial databases such as geographic information systems (GIS) range searching obviously plays a pivotal role, and a large number of external data structures (indexing schemes) for answering such queries have been developed (see [20, ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter, Indexing for data models with constraints and classes, Journal of Computer and System Sciences, 52:589--612, 1996.

....O(log N) internal memory search term, and an O(T=B) reporting term accounting for the O(T=B) I Os needed to report T elements. Recently, the above bounds have been obtained for a number of problems (e. g [30, 26, 149, 5, 47, 87] but higher lower bounds have also been established for some problems [141, 26, 93, 101, 106, 135, 102]. We discuss these results in later sections. B trees come in several variants, like B and B trees (see e.g. 35, 63, 95, 30, 104, 3] and their references) A basic B tree is a Theta(B) ary tree (with the root possibly having smaller degree) built on top of Theta(N=B) leaves. The degree of ....

....B we lose when charging the construction of a structure of size B i to only B i Gamma1 objects is offset by the 1=B factor in the construction bound. Deletions can also be handled I O efficiently using a global rebuilding idea. 4. 2 Optimal dynamic structure Following several earlier attempts [101, 127, 141, 43, 98], Arge et al. 26] developed an optimal dynamic structure for the 3 sided planar range searching problem. The structure is an external version of the internal memory priority search tree structure [113] The external priority search tree consists of a base B tree on the x coordinates of the N ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for data models with constraints and classes. Journal of Computer and System Sciences, 52(3):589--612, 1996.

....optimal cost when actually scanning the pointed items as there is no replication of data. Namely, if each item occupies b bytes, we require optimal O(br=B) I Os to scan the content of all retrieved items. This is not always possible with the known multidimensional data structures as pointed out in [24]. We require that our external memory algorithms must use optimal storage. In our case, the space required is linear, i.e. O(N=B) disk blocks. In general, we have to face some problems that do not appear in internal memory, especially when we have to maintain dynamically data structures with ....

.... range queries on ordered decomposable problems can be done with quad trees [15] k d trees [7] and many other data structures, such as grid les [30] space lling curves, e.g. 32] hB trees [28] and R trees [20] just to cite a few (see the survey [16] for a more complete scenario and paper [24] for a discussion of their worst case complexity) These data structures were originally designed to support some operations for windowing problems in computer graphics and databases, and in several cases it was rather dicult to keep them balanced [35, 40] In contrast, our cross tree can be ....

[Article contains additional citation context not shown here]

P.C. Kanellakis, S. Ramaswamy, D.E. Vengro and J.S. Vitter. Indexing for data models with constraints and classes. Journal of Computer and System Sciences 52 (1996), 589-612.

....simple class attribute (such as the string, integer or boolean) A nested predicate is issued against a nested attribute which contains a reference to an object in the domain of another class. The most intensively studied query type was indexing an inheritance hierarchy against a simple attribute [KKD89, LOL92, KiMo94, KaRa94]. The class hierarchy index (CHindex) KKD89] is based on B trees and maintains a single index for all classes in the inheritance hierarchy. The CHindex efficiently performs match point operations, but is not optimal for range queries. Conversely, H index [LOL92] efficiently performs range ....

....efficiently performs range operations and reads more pages than CH index when match point queries are executed. As a result, a method combining the advantages of both CHand H trees has been proposed in [KiMo94] A different approach to the problem has been proposed by Kanellakis, Ramaswamy at al. [KaRa94]. They reduced the indexing a simple class attribute to external dynamic 2dimensional range searching and exploited data structures studied in computational geometry. Most techniques for indexing an aggregation hierarchy [BeKi89, BG93, Ber94, BeFo95] originate from the join index structure ....

Kanellakis, P., Ramaswamy, S., Vengroff, D., Vitter, J., 1994, Indexing for Data Models with Constraints and Classes. Tech. Report CS-94-09., Brown University, 39 pp.

No context found.

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, J. S. Vitter. Indexing for Data Models with Constraints and Classes. Proc. 12th ACM PODS, 233--243, 1993. To appear in JCSS.

No context found.

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, J. S. Vitter. Indexing for Data Models with Constraints and Classes. Proc. 12th ACM PODS, 233--243, 1993.

....was supported in part by National Science Foundation grant CCR 9007851 and by Army Research Office grant DAAL03 91 G 0035. Large scale problems involving geometric data are ubiquitous in spatial databases [24,32,33] geographic information systems (GIS) 10,24,33] constraint logic programming [19,20], object oriented databases [38] statistics, virtual reality systems, and computer graphics [33] As an example, NASAls soon to be petabyte sized databases are designed to facili tate a variety of complex geometric queries [10] Im portant operations on geometric data include range queries, ....

....Is there a data structure for 2 d on line range queries that achieves O(log B , I Os for updates and range queries using O( blocks of space (The off line version of the problem is solved optimally in this paper. Updates and three sided range queries can be handled by metablock trees [20] in O(logB , logB ) I Os using O( space. Two sided range queries anchored on the diagonal can be done in O(log , I Os per query and O(log , log , 2 B) I Os per (semidynamic)insert [20] Can an N vertex polygon be triangulated using O(N B) I Os Under what conditions Can we find all ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff & J. S. Vitter, "Indexing for Data Models with Constraints and Classes," Proc. 12th ACM PODS', Washington, DC (1993).

....an algorithm in terms of the number of I Os it performs and the number of disk block it uses. The dynamic interval management problem is the problem of maintaining a set of intervals such that, given a query interval I q , all intervals intersecting I q can be reported eciently. As discussed in [28, 29, 37], the problem is crucial for indexing constraints in constraint databases and in temporal databases. The key component of dynamic interval management is the ability to answer stabbing queries [29] Given a set of intervals, a stabbing query with a point q asks for all intervals containing q. By ....

....query interval I q , all intervals intersecting I q can be reported eciently. As discussed in [28, 29, 37] the problem is crucial for indexing constraints in constraint databases and in temporal databases. The key component of dynamic interval management is the ability to answer stabbing queries [29]. Given a set of intervals, a stabbing query with a point q asks for all intervals containing q. By representing an interval [x; y] as the point (x; y) in the plane, a stabbing query reduces to the special case of two sided 2 dimensional range searching called diagonal corner queries with corner ....

[Article contains additional citation context not shown here]

P. C. Kanellakis, S. Ramaswamy, D. E. Vengro, and J. S. Vitter. Indexing for data models with constraints and classes. Journal of Computer and System Sciences, 52(3):589-612, 1996. 20

No context found.

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for data models with constraints and classes. In Proc. ACM Symp. on Principles of Database Sys., pages 233--243, 1993.

No context found.

Paris C. Kanellakis, Sridhar Ramaswamy, Darren Erik Vengro#, and Je#rey Scott Vitter. Indexing for data models with constraints and classes. J. Comput. Syst. Sci, 52(3):589--612, 1996.

No context found.

P. Kanellakis, S. Ramaswamy, D. Vengroff, and J. Vitter. Indexing for Data Models with Constraints and Classes. In Proc. of PODS, pages 233--243, 1993.

No context found.

P. C. Kanellakis, S. Ramaswamy, D. E. Vengro#, J. S. Vitter. Indexing for Data Models with Constraints and Classes. J. Computer and System Sciences, 52(3), pp. 589--612, 1996.

No context found.

P. Kanellakis, S. Ramaswamy, D. Vengroff and J. Vitter. Indexing for Data Models with Constraint and Classes. In Proc. 12th ACM SIFACT-SIGMOD-SIGART Symposium on Principles of Database Systems,pages 233-243 , Washington, D.C, 1993.

No context found.

P. C. Kanellakis, S. Ramaswamy, D. E. Vengroff, and J. S. Vitter. Indexing for Data Models with Constraints and Classes. Journal of Computer and System Sciences, 52(3):589--612, 1996.

No context found.

P. C. Kanellakis, S. Ramaswamy, D. Vengro#, E., and J. S. Vitter. Indexing for data models with constraints and classes. In Proc. ACM Symp. Principles of Database Systems, pages 233--243, 1993.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC