Actual Query Research Articles

Database design in the modern organization—identifying robust structures under changing query patterns and arrival rate conditions

We summarize the problem tackled here in the following way: Given a modern database application environment, how can we identify and select the database structure that provides robust performance across changing query patterns and arrival rate conditions? We demonstrate the importance of investigating the underlying relationships and then utilize this information in formulating robust structures. Our work is pre-theory in the philosophy of science sense. That is, the careful identification and observation of relationships will subsequently be utilized in formulating a testable theory of the development of robust database structures under dynamic query patterns and arrival rates. Our first step in providing a database design or “structure selection” method is to determine potential good performers among different database structures. These potential good performers are selected and analyzed across arrays of query patterns. The next step is to identify database structures that are robust structures, that is good performers across the different types of query patterns and arrival rate levels. The presentation includes illustrations of the determination of actual query pattern processing times and the use of these times within a queuing analysis. In fact, for the database layout analyzed, application of our methods demonstrates the existence of such robust database structures.

LAMS(TM)--a laboratory animal management system.

The LAMStrade mark database stores data on a colony of breeding animals. Forms are hierarchial and show details of internal codes, matings, litters, and offspring. The identifier given to each animal can be subdivided as such. Each form shows an abbreviated list of the related data from the form one level down, and some special fields, when double-clicked, cause the related record to be displayed. The print button allows the user to print the current record and its related records. Other buttons on each form allow the user to amend, delete, find, and add records within certain rules. User-defined lists are created to allow the selection of various characteristics during data entry. The offspring form contains a section where the user can define the label of a comment and/or a text field. This name is then always subsequently available as an option in a list of user-defined labels. Reports are available for tailtipping dates (if applicable) and calculation of genetic ratios. A queryform allows the user to filter the records in the offspring form to the criteria specified, and a display of the actual query submitted is shown. An integrated HELP is available. The LAMStrade mark database is available at http://www.hgu.mrc.ac.uk/Softdata/Lams

Processing Ill-defined Spatial Operators in Geographic Databases

A common-sense based approach to develop processing strategies for resolving queries that contain imprecise spatial operators is detailed. The Naive Geography approach, introduced by Egenhofer and Mark in 1995 is an important motivation for this study. The context of the study is a geographic database of the United States. The imprecise operators studied are “close_to,†“between,†and “adjacent_to.†These operators are considered “imprecise†because they do not have a clear and precise definition in the literature. Previous studies are reviewed. An objectoriented geographic data model that was developed earlier by the researchers is discussed. The data model is used as a foundation for developing algorithms for processing the imprecise operators. Detailed common-sense based algorithms for resolving queries containing the above imprecise operators are discussed. The model and the algorithms are implemented using real-life data. Details of the implementation, along with actual query results are presented. Finally, the author’s conclusions, along with some caveats in this type of research, and possible future research are discussed.

The suffix-signature method for searching for phrases in text

We present a new algorithm to find all occurrences of a given phrase based on the data structure known as a suffix array and using a corresponding array of signatures. With this algorithm, matches to phrases of moderate length can be found with expected search time of one disk access to the text and one disk access to its index. To achieve this performance for phrases of up to five words in length requires an index having total size of approximately 120% of the size of the text. The algorithm guarantees a worst case search performance of two disk accesses to the text per phrase search. Experiments with actual data ranging in size from 6Mb to 550Mb and with actual query patterns derived from logs of searches on the World Wide Web show that the approach is applicable in practice to a variety of texts and realistic phrase searches.

Block concatenated signatures for partial match retrieval

In this paper, a block concatenated signature (BCS) file scheme is proposed to speed up partial match retrieval in very large databases. A BCS is generated for each block of the data file by hashing the attribute values in the data block. Then the BCSs form a signature file which is used as an index to the data file. For a partial match retrieval query, a block query signature (BQS) is generated and compared with the BCSs. Only those data blocks whose corresponding BCSs match the BQS are retrieved from secondary storage and compared with the actual query. The size of the BCS file is usually less than 15% of the size of the data file, and usually only a subset of each BCS is accessed. Compared to the record signature schemes, the proposed BCS scheme has better performance because the number of signature blocks to be accessed is much smaller per query. Thus, we can obtain considerable speedup in partial match retrieval by using the BCS file. The storage requirement and the performance of the BCS file are evaluated and compared with those of other signature file schemes.

Adaptive selection of query execution strategies by learning automata

The traditional approach to evaluate query execution strategies using approximate cost models may be inadequate for particular environments. For instance, if the environment does not satisfy the assumptions made by the cost model, the cost estimates can be so distorted that expensive strategies will be chosen. We propose a new approach for choosing execution strategies based on the actual cost history of query execution under various strategies, rather than on assumption-loaded estimates of these costs. Adaptive selection automatically changes the strategies selected, tracking cost variations caused by changes in the database state and query load. Furthermore, it does not require any assumptions about internal database structures, data characteristics, or distribution of queries. Queries are divided into query classes, where all queries in a class share the same execution strategies. A learning automaton is then used for each class to infer over time which are the current best strategies, based on actual query execution costs. We show the results of running the adaptive selector using real query loads for an existing database.

Dynamic user profiles and flexible queries in office document retrieval systems

The problem of document management in an Office Information System in view of supporting the decision process by a set of users with different knowledge, capabilities and requirements is considered. User profiles are used to improve the retrieval properties and a control system is outlined to automatically update the utility information used by the searching strategies. user requirements are modeled in Dynamic User Profiles in order to maintain accurate knowledge of the most promising approach to document search, of the interest fields, and of the favourite output format. An example of DUP use is provided adopting a flexible query specification to take advantage of database selection, retrieval via keyword, or full text scanning in a multi-strategy query processing approach. Actual queries are redefined by extracting the aspects suitable for each strategy.

Multiple-query optimization

Some recently proposed extensions to relational database systems, as well as to deductive database systems, require support for multiple-query processing. For example, in a database system enhanced with inference capabilities, a simple query involving a rule with multiple definitions may expand to more than one actual query that has to be run over the database. It is an interesting problem then to come up with algorithms that process these queries together instead of one query at a time. The main motivation for performing such an interquery optimization lies in the fact that queries may share common data. We examine the problem of multiple-query optimization in this paper. The first major contribution of the paper is a systematic look at the problem, along with the presentation and analysis of algorithms that can be used for multiple-query optimization. The second contribution lies in the presentation of experimental results. Our results show that using multiple-query processing algorithms may reduce execution cost considerably.

A Block Structured Query Language for accessing a relational data base

This paper describes a B lock S tructured Q uery L anguage (BSQL) to be used with relational data bases. The syntax of the language is presented and discussed. Facilities of the language are illustrated by examples of actual queries. In particular we demonstrate the ability of BSQL to obtain useful information from a relational data base that is incomplete. A relational calculus is then presented which forms a basis for a formalism which precisely describes the semantics of BSQL. Finally, comparative examples with other query languages are given.