Query processmg can be sped up by keeping frequently accessed users' views materlahzed However, the need to access base relations m response to queues can be avoided only If the materlahzed view ls adequately maintainedWe propose a method m which all database updates to base relations are first filtered to remove from consideration those that cannot possibly affect the view The condltlons given for the detection of updates of this type, called arrelevant updates, are necessary and sufficient and are mdependent of the database state For the remammg database updates, a dzfferentlal algonthm can be apphed to re-evaluate the view expression The algonthm proposed exploits the knowledge provided by both the view defimtlon expression and the database update operations
Query processmg can be sped up by keeping frequently accessed users' views materlahzed However, the need to access base relations m response to queues can be avoided only If the materlahzed view ls adequately maintainedWe propose a method m which all database updates to base relations are first filtered to remove from consideration those that cannot possibly affect the view The condltlons given for the detection of updates of this type, called arrelevant updates, are necessary and sufficient and are mdependent of the database state For the remammg database updates, a dzfferentlal algonthm can be apphed to re-evaluate the view expression The algonthm proposed exploits the knowledge provided by both the view defimtlon expression and the database update operations
Hypertext and other page-oriented databases cannot be schematized in the same manner as recordoriented databases. As a result, most hypertext databases implicitly employ a data model based on a simple, unrestricted graph. This paper presents a hypergraph model for maintaining page-oriented databases in such a way that some of the functionality traditionally provided by database schemes can be available to hypertext databases. In particular, the model formalizes identification of commonality in the structure, set-at-a-time database access, and definition of user-specific views. An efficient implementation of the model is also discussed.A simple model implicit in many hypertext systems is one based on labelled directed graphs (see, e.g., [7]). In such a model, a database state is a triple: w, 4 E),
Most contemporary database systems perform cost-based join enumeration using some variant of System-R's bottomup dynamic programming method. The notable exceptions are systems based on the top-down transformational search of Volcano/Cascades. As recent work has demonstrated, bottom-up dynamic programming can attain optimality with respect to the shape of the join graph; no comparable results have been published for transformational search. However, transformational systems leverage benefits of top-down search not available to bottom-up methods.In this paper we describe a top-down join enumeration algorithm that is optimal with respect to the join graph. We present performance results demonstrating that a combination of optimal enumeration with search strategies such as branch-and-bound yields an algorithm significantly faster than those previously described in the literature. Although our algorithm enumerates the search space top-down, it does not rely on transformations and thus retains much of the architecture of traditional dynamic programming. As such, this work provides a migration path for existing bottom-up optimizers to exploit top-down search without drastically changing to the transformational paradigm.
Many documents with mathematical content are published on the Web, but conventional search engines that rely on keyword search only cannot fully exploit their mathematical information. In particular, keyword search is insufficient when expressions in a document are not annotated with natural keywords or the user cannot describe her query with keywords. Retrieving documents by querying their mathematical content directly is very appealing in various domains such as education, digital libraries, engineering, patent documents, medical sciences, etc. Capturing the relevance of mathematical expressions also greatly enhances document classification in such domains.Unlike text retrieval, where keywords carry enough semantics to distinguish text documents and rank them, math symbols do not contain much semantic information on their own. In fact, mathematical expressions typically consist of few alphabetical symbols organized in rather complex structures. Hence, the structure of an expression, which describes the way such symbols are combined, should also be considered. Unfortunately, there is no standard testbed with which to evaluate the effectiveness of a mathematics retrieval algorithm.In this paper we study the fundamental and challenging problems in mathematics retrieval, that is how to capture the relevance of mathematical expressions, how to query them, and how to evaluate the results. We describe various search paradigms and propose retrieval systems accordingly. We discuss the benefits and drawbacks of each approach, and further compare them through an extensive empirical study.
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