A Learning Approach to SQL Query Results Ranking Using Skyline and Users' Current Navigational Behavior

Chen, Zhiyuan; Li, Tao; Sun, Yanyu

doi:10.1109/tkde.2012.128

Cited by 7 publications

(4 citation statements)

References 26 publications

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“…A cyclic graph based approach was developed [13] for optimizing the linear recursive queries in SQL, but it failed to detect the duplicate queries in the SQL with higher accuracy. An SVM based ranking method was introduced [14] to rank the SQL query results, which functioned with limited and heterogeneous users preferences.…”

Section: Related Workmentioning

confidence: 99%

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

Ramakrishnan¹,

Palanisamy²

2020

Turk J Elec Eng & Comp Sci

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Discovery of antipatterns from arbitrary SQL query log depends on the static code analysis used to enhance the quality and performance of software applications. The existence of antipatterns reduces the quality and leads to redundant SQL statements. SQL log includes a large load on the database and it is difficult for an analyst to extract large patterns in a minimal time. Existing techniques which discover antipatterns in SQL query face a lot of innumerable challenges to discover the normal sequences of queries within the log. In order to discover the antipatterns in the log, an efficient technique called Brown infomax boosted SQL query clustering (BIBSQLQC) technique is introduced. Initially, the number of patterns (i.e. queries) are extracted from the SQL query log. After extracting the patterns, the ensemble clustering process is carried out to find out the antipatterns from the given query log. The Brown infomax boost clustering is an ensemble learning method for grouping the patterns by constructing several weak learners. The Brown clustering is used as a weak learner for partitioning the patterns into 'k' number of clusters based on the Euclidean distance measure. Then the weak learner merges the two clusters with maximum information gained to minimize the time complexity. The clustering results of weak learners are combined into strong results with minimal error rate (ER). By this way, the antipattern in the SQL query log is detected with a higher accuracy. Experimental evaluation is conducted with different parameters namely detection accuracy (DA), false positive rate (FPR) and time complexity (TC) using the two SQL query log data-sets (DS). The experimental result shows that, the BIBSQLQC technique achieves higher DA with lower TC and FPR than the conventional methods.

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Section: Related Workmentioning

confidence: 99%

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

Ramakrishnan¹,

Palanisamy²

2020

Turk J Elec Eng & Comp Sci

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“…Chen [1] used an approach that aims at relational data. It uses training set examples inferred from user navigational behaviour and skyline.…”

Section: Fig 1: User Navigational Behaviourmentioning

confidence: 99%

A Survey on Ranking SQL Queries Using Skyline and User Preferences

.¹

2014

IJRET

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Databases consist of large sets of data, finding relevant data among it is a tedious task. However when a user enters the query in a user interface, it results in much of the extraneous data. This extraneous data is not relevant to user. The solution to this problem is to obtain a set of non dominated records called skyline records and rank the records according to user preferences. The purpose of this survey paper is to illustrate how skyline operation is implemented and how to capture user preferences. Various techniques of user preferences and algorithms for skyline have been elaborated in this paper. To navigate the user according to their preferences clustering techniques are also used. Clustering can be done by group by operator, by clustering techniques, and by automatic categorization methods. Navigation tree is obtained on the basis of the clusters. A study of capturing users' preferences and navigating results to user is described in this paper.

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“…Also, algorithms for consistency checking, optimistic selection and caution selection were proposed [12]. Chen et al, proposed an approach to rank SQL queries results using users' navigational behaviour [13]. Wang et al, provided algorithms for shared data access in clouds.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Data Management Policy For Heterogeneous Mobile Databases

2020

IJETER

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Mobile databases have spread into today's communication and computing, with numerous applications relying on mobile technology. It is an ever growing field and very much in demand, thus proving to be an excellent area to carry out research. With a large amount of data, management of such data is the key issue. Existing policies consist of Message Digest algorithms used along with cache replacement algorithms. However, the Message Digest algorithms have a major drawback of data replication, leading to a large overhead on the memory constraints. This report discuses a data management policy for heterogeneous mobile databases. This policy involves a Synchronization Server, which contains only the most critical of data, instead of all the data as in case of a normal server. The synchronization server data is efficiently managed by classifying it under various states based on the frequency of data access, the time of data access, i.e., which data has been actively used over a period of time and which has not been used so. This aims to facilitate synchronization of data between heterogeneous databases using cache replacement and to improve the rate of synchronization between heterogeneous mobile databases and the database servers. This policy has shown better performance than Least Recently Used and Most Frequently Used algorithms in terms of the execution time.

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A Learning Approach to SQL Query Results Ranking Using Skyline and Users' Current Navigational Behavior

Cited by 7 publications

References 26 publications

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

BIBSQLQC: Brown infomax boosted SQL query clustering algorithm to detect anti-patterns in the query log

A Survey on Ranking SQL Queries Using Skyline and User Preferences

An Adaptive Data Management Policy For Heterogeneous Mobile Databases

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