Data analysis for query processing

Robinson, Jerome; Lowden, Barry G. T.

doi:10.1007/bfb0052861

Cited by 9 publications

(6 citation statements)

References 6 publications

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“…In previous work [12,15,16,17] on 'semantic query optimisation' [10,18,19,20] the authors developed a particular type of subset descriptor, which the data server used for query optimisation (rewriting the query in a form that is answerable more quickly). The current paper extends the use of rules for query optimisation, by utilizing descriptors in the Server to provide new subset descriptors for remote client caches, and so extends current research on 'associative caching' [e.g.…”

Section: Discussionmentioning

confidence: 99%

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Extending the Re-use of Query Results at Remote Client Sites

Robinson

Lowden

2000

Lecture Notes in Computer Science

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Abstract. In 'Associative Caching' each client computer keeps a copy of its query result sets in its own local database. The purpose is to reduce the size and frequency of queries to the remote data server accessed by wide-area network. For each new query the client tries to find some or all of the required data in its local collection of result sets. This is done by syntactic comparison of the new query with each previous query, to detect overlapping data sets. Attribute-Pair Range Rules are subset descriptors which the server derives from its data, for its own use in query optimisation. These subset descriptors can be further utilized to provide descriptors for each query result set. This new information enables clients to exploit their cached data for syntactically unrelated queries.

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Section: Discussionmentioning

confidence: 99%

“…It uses them for query reformulation to provide faster query processing, as explained in [12,15,16,17] for example. Deriving cache descriptors from them is therefore an extension to the value of an existing system.…”

Section: Creating Subset Descriptions Of Server Datamentioning

confidence: 99%

Extending the Re-use of Query Results at Remote Client Sites

Robinson

Lowden

2000

Lecture Notes in Computer Science

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“…It can be used for structured data analysis of large sets. The limitations of Map Reduce as given in [10] are it produces the necessary secondary indices in an offline batch manner. Hence, secondary indexes are not up-todate.…”

Section: Related Workmentioning

confidence: 99%

Assessment and Optimization of User Imprecise Queries in Cloud Environments

Mohankumar¹,

Balamurugan²

2017

IJIES

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Data size growth rate arose massively day to day due to database and internet applications. It has become a challenging task to organize those data and to provide the user a relevant data in time with correct and compatible manner. Further choice was a pervasive feature of social life that profoundly affects people. They work with assumptions that stored data represent the proper subset of real world data and make a quick decision based on imprecise knowledge in daily life for survivals which tends to get irrelevant output .Sometimes for other input, this may be exact so it should not remove instead, managed to utilize appropriately to minimize the processing time. Moreover, optimism significance relies on user satisfactions. This paper provides a vision to tackle these issues by assessing the imprecise incoming query and reutilizing for future user instead of rejecting as wrong or irrelevant. To address optimization issues in this paper, we proposed the techniques for optimizing the queries to provide customers with fast data retrieval. In our model, Query processing: A 3-step process that transforms a high-level query (MongoDB) into an equivalent and more efficient lower-level query (relational algebra). Further MEAN stack based cooperative semantic approach was deployed in cloud environments as novelty to provide solution with the level of performance significance.

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“…Metadata, in the form of pairs of range conditions from different attributes can be easily derived from the data, either by induction triggered by queries [4] or by systematic analysis [6]. The resulting attribute-pair rules can be used for Semantic Query Optimisation and cache management in remote clients.…”

Section: Introductionmentioning

confidence: 99%

“…Methods for choosing appropriate subsets include deriving rules by systematic analysis and set reduction as in [6], or by associating a rule with each bar of a histogram describing the table as in [7], or by recognizing subsets that are frequently used during database access. Some properties of AP Rules are now specified.…”

Section: Introductionmentioning

confidence: 99%

Lecture Notes in Computer Science 1205

Bollen

1999

Industrial Robot: An International Journal

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Abstract. This paper examines the properties of metadata in the form of IF THEN rules which contain two predicates on attributes of a relational database table. For example: a(15 .. 30) ⇒ d(243 .. 271), which means "if the value of attribute 'a' in a tuple is in the range 15 to 30 then the value of attribute 'd' will be in the range 243 to 271." Metadata of this kind is useful in Semantic Query Optimisation and Remote Cache Management. The two predicates (antecedent and consequent) in each rule are Selection Conditions or constraints of the type found in database queries. Each condition therefore denotes a subset of a database table. Rules can be cascaded, using subrange containment as the link between successive rules. The set of rules can therefore be regarded as a set of edges in a Condition Dependency Graph, and using the rule-set is path discovery in the graph. The purpose of the current paper is to introduce some of the properties of attribute-pair range rules.

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Data analysis for query processing

Cited by 9 publications

References 6 publications

Extending the Re-use of Query Results at Remote Client Sites

Extending the Re-use of Query Results at Remote Client Sites

Assessment and Optimization of User Imprecise Queries in Cloud Environments

Lecture Notes in Computer Science 1205

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