L. Fegaras, D. Maier "Optimizing Object Queries Using an Effective Calculus" ACM Transactions on Database systems, Vol. 25, N 4, December 2000,pp. 457-516  Home/Search   Document Details and Download   Summary   Related Articles   Check

....is based on a global as view approach. Any ground term which appears in the rewritten query must be mapped to source constructs in the Ontologyto Object Model (OOM) mapping. The query translation phase translates the rewritten query into an expression in Fegaras Monoid Comprehension Calculus [11]. The calculus proviudes a state of the art semantics for OODBs together with well founded tools for optimization and evaluation of queries. We have enriched the calculus with a match operator to perform object fusion. The match operator supports the reconstruction of a unique object by ....

....on it. The ontology is used at this stage to improve the calculus expression by identifying redundant generators (potential iterations) over source extents. The calculus to algebra translation phase. From this point on, the translation system is an adaptation of Fegaras OQL optimizer [11]. The calculus expression is translated into an expression in a logical query algebra based on the nested relational algebra. The translation rules are quite general and do not depend on source specific information. The logical and physical optimization phases are essentially those presented in ....

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L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 2001. (to appear).

.... select struct(price: p2.SalePrice, area: p2.Area, street: p2.StreetNumName) from Property p1, Property p2 where p1.landInfo.landUse = p2.landInfo.landUse and p1.Suburb = p2.Suburb and p1.StreetNumName= 18 Duff Pl A plan is generated for this query based on the monoid comprehension calculus [2]. There is still additional work to perform the plan needs to be unnested and converted into an exact set of steps to be sent to the executor together with a description of the data flow. Details of this process are beyond the scope of this paper. The resulting physical query plan is rather ....

L. Fegaras and D. Maier. Optimizing object queries using an effective calculus, 1998. Available online at http://lambda.uta.edu/monoid.ps.gz.

....way of expressing a great variety of queries; for instance, operations over multiple collection types, group by operations, existential or universal quantification have very similar calculus representations. The calculus framework of this dissertation has extended previous research [Afs98, FM95, FM00] in order to deal with several weak points of OQL and to resolve a number of ambiguities. One of these extensions concerns the introduction of new normalization rules wrt [FM00] Normalization is the transformation (unnesting) of monoid expressions to syntactically equivalent ones which are ....

....calculus representations. The calculus framework of this dissertation has extended previous research [Afs98, FM95, FM00] in order to deal with several weak points of OQL and to resolve a number of ambiguities. One of these extensions concerns the introduction of new normalization rules wrt [FM00] Normalization is the transformation (unnesting) of monoid expressions to syntactically equivalent ones which are either more efficient, or provide opportunities for further calculus or algebraic optimization. A set of algebraic operators (e.g. Reduce, Select, Nest) are proposed as the second ....

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L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 2000.

....(c) multi node physical plan processor. In the second phase, the sequential query plan is divided into several partitions or subplans which are allocated machine resources by the scheduler. Figure 3(a) depicts a plan for the example query expressed in the logical algebra of Fegaras and Maier [4], which is the basis for query optimisation and evaluation in Polar . The logical optimiser performs various transformations on the query, such as fusion of multiple selection operations and pushing projects (called reduce in [4] and in the figures) as close to scans as possible. The physical ....

....example query expressed in the logical algebra of Fegaras and Maier [4] which is the basis for query optimisation and evaluation in Polar . The logical optimiser performs various transformations on the query, such as fusion of multiple selection operations and pushing projects (called reduce in [4] and in the figures) as close to scans as possible. The physical optimiser transforms the optimised logical expressions into physical plans by selecting algorithms that implement each of the operations in the logical plan (Figure 3(b) For example, in the presence of indices, the op timiser ....

L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, December 2000.

....(proteins) p.proteinlblast) operation call (blast(p.sequenceJ) b) c) Fig. 3. Example query: a) single node logical plan, b) single node physical plan (c) multi node physical plan. Figure 3(a) depicts a plan for the example query expressed in the logical algebra of Fegaras and Maier [4], which is the basis for query optimisation and evaluation in Polar . The logical optimiser performs various transformations on the query, such as fusion of multiple selection operations and pushing projects (called reduce in [4] and in the figures) as close to scans as possible. The physical ....

....example query expressed in the logical algebra of Fegaras and Maier [4] which is the basis for query optimisation and evaluation in Polar . The logical optimiser performs various transformations on the query, such as fusion of multiple selection operations and pushing projects (called reduce in [4] and in the figures) as close to scans as possible. The physical optimiser transforms the optimised logical expressions into physical plans by selecting algorithms that implement each of the operations in the logical plan (Figure 3(b) For example, in the presence of indices, the op timiser ....

L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 24(4):457-516, December 2000.

....constructs. Queries are mapped to a spario historical calculus and then to a spatio historical algebra. These mappings provide several opportunities for optimization using rewrite rules that are extensions to the techniques used by Fegaras and Maier for optimizing object query languages [6]. Tripod s language bindings and its extended OQL use the services provided by the extended object model to access and manipulate historical, spatial and aspatial data. This paper is structured to reflect Tripod s layered data model, concentrating in particular on the ways in which each layer ....

....where each element is a closed interval (i.e. the delimitlug instants are included) Simple integer values are used rather than calendar based dates. Although all examples in this paper use the Time Intervals type, they are equally applicable to the Instants type. A single state is norated as ([1 5,6 9],r , where r is, e.g. a snapshot value from the domain of Regions that holds between the granules i to 5 and 6 to 9, inclusively. A history is norated as exemplified by (V, 8, 1 3, 7 9] r) 4 5] r, r3, r4) 10 14] r, r4) with V = bag(Regions , 8 = TimeIntervals, and ff = ....

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L. Fegaras and D. Mater. Optimizing Object Queries Using an Effective Calculus. ACM TODS, 25(4):457-516, 2000.

....these processors serves as a coordinator, running the compiler optimizer, while the remaining seven serve as object stores, running an object manager and execution engine. In Polar, OQL queries are compiled into parallel query execution plans (PQEPs) expressed in an object algebra based on that of [5]. All the algebraic operators are implemented as iterators [7] As such, the operators support three main functions, open, next and close, which define the main interface through which they interact with one another. The implementation of the algebra is essentially sequential, and most of the ....

L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, December 2000.

....is based on a global as view approach. Any ground term which appears in the rewritten query must be mapped to source constructs in the Ontology to Object Model (OOM) mapping. The query translation phase translates the rewritten query into an expression in Fegaras Monoid Comprehension Calculus [10], enriched with a match( operator to perform object fusion. The match( operator supports the reconstruction of a unique object by gathering sparse information coming from one or more sources. The translation process works in a compositional fashion, using the OOM mapping to translate DL terms, ....

....on it. The ontology is used at this stage to improve the calculus expression by identifying redundant generators (potential iterations) over source extents. The calculus to algebra translation phase. From this point on, the translation system is an adaptation of Fegaras OQL optimizer [10]. The calculus expression is translated into an expression in a logical query algebra based on the nested relational algebra. The translation 2 rules are quite general and do not depend on sourcespecific information. The logical and physical optimization phases are essentially those presented ....

[Article contains additional citation context not shown here]

L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 2001. (to appear).

....from Histories inwards represents a spatio historical object model. This, however, leaves open the question as to how the resulting model can be queried, and how these queries can both be given a precise semantics and be effectively optimized. In this regard, the monoid comprehension calculus of [8] is extended in Tripod to accommodate the querying of spatial and temporal data. In Figure 1, this is illustrated by the layers representing the query algebra and the query calculus, the former being derived from the latter using mappings described in [8] The focus of this paper is on the query ....

....the monoid comprehension calculus of [8] is extended in Tripod to accommodate the querying of spatial and temporal data. In Figure 1, this is illustrated by the layers representing the query algebra and the query calculus, the former being derived from the latter using mappings described in [8]. The focus of this paper is on the query calculus layer in Figure 1. This layer is important, in that it provides a semantics for historical queries in object databases, and also because its incorporation within the monoid comprehension calculus of [8] allows the reuse of the associated query ....

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L. Fegaras and D. Maier. Optimizing Object Queries Using an Effective Calculus. ACM TODS, 25(4), December 2000.

....queries [CM95b, CM95a] while others are focused on handling encapsulation and methods [DGK 91] However, there very few proposals on query optimization in the presence of object identity and destructive updates, features often supported by most realistic OODB languages. In earlier work [FM98, FM95b, FM95a] we proposed an effective framework with a solid theoretical basis for optimizing OODB query languages. Our calculus, called the monoid comprehension calculus, has already been shown to capture most features of ODMG OQL [Cat94] and is a good basis for expressing various ....

....8 extends our framework to capture database updates and discusses how this theory can be applied to solve the view maintenance problem. 2 Background: The Monoid Comprehension Calculus This section summarizes our earlier work on the monoid calculus. A more formal treatment is presented elsewhere [FM98, FM95b, FM95a] The monoid calculus is based on the concept of monoids from abstract algebra. A monoid of type T is a pair ( Phi; Z Phi ) where Phi is an associative function of type T Theta T T (i.e. a binary function that takes two values T and returns a value T ) called the accumulator ....

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L. Fegaras and D. Maier. Optimizing Object Queries Using an Effective Calculus. Submitted to TODS. Available at http://www-cse.uta.edu/¸fegaras/monoid.ps.gz. August 1998.

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L. Fegaras, D. Maier "Optimizing Object Queries Using an Effective Calculus" ACM Transactions on Database systems, Vol. 25, N 4, December 2000,pp. 457-516

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Leonidas Fegaras and David Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 25(4):457--516, 2000.

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L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 2001. (to appear).

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Leonidas Fegaras and David Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 25(4):457--516, 2000.

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L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 2001. (to appear).

No context found.

L. Fegaras and D. Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 2001. (to appear).

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L. Fegaras and D. Maier, "Optimizing Object Queries Using an Effective Calculus," ACM Transactions on Database Systems, 25 (4), pp. 457-516, 2000.

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Leonidas Fegaras and David Maier. Optimizing object queries using an effective calculus. ACM Transactions on Database Systems, 25(4):457--516, 2000.

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7 Leonidas Fegaras and David Maier. Optimizing Object Queries Using an Effective Calculus. ACM Transactions on Database Systems (TODS), 25(4):457-- 516, 2000.

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L. Fegaras and D. Maier. Optimizing Object Queries Using an Effective Calculus. ACM TODS, 25(4), December 2000.

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