Clustering-based fragmentation and data replication for flexible query answering in distributed databases

Wiese, Lena

doi:10.1186/s13677-014-0018-0

Cited by 19 publications

(6 citation statements)

References 26 publications

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“…Hence a gapweighted string subsequence kernel algorithm was developed [7] to detect the subsequences of query strings and categorize indefinite test queries using support vector machine which resulted in failure in minimizing the error of the classification. Finally clustering-based fragmentation method was developed [8] for solving the data replication problem from the SQL query but, TC of the clustering remained unsolved.…”

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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“…In previous work [3], a clustering procedure was applied to partition the original tables into fragments based on a relaxation attribute chosen for anti-instantiation. For this we used a notion of similarity between to constants; this similarity can be deduced with the help of an ontology or taxonomy in which the values are put into relation.…”

Section: Relaxation Attributesmentioning

confidence: 99%

“…First we consider only a single fragmentation (for example, as in [3] obtained for a single relaxation attribute). When doing m-way replication, m copies of the fragments obtained for the single fragmentation are replicated.…”

Section: Replication For a Single Frag-mentationmentioning

confidence: 99%

“…• A similarities table stores for each head term (column head ) and each other term (column term) that occurs in the active domain of the corresponding relaxation attribute a similarity value between 0 and 1 (column sim) There are different metrics for calculating this similarity value. An overview is given in [3]. We used the path length scheme.…”

Section: Experimental Study In a Larger Clustermentioning

confidence: 99%

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A Replication Scheme for Multiple Fragmentations with Overlapping Fragments

Wiese

Waage

Bollwein

2016

The Computer Journal

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In this article, we introduce a replication procedure in a distributed database system that supports several fragmentations of the same data table. One application that requires multiple fragmentations is flexible (similarity-based) query answering. The major feature of our replication procedure is that replication and recovery respect the overlaps of fragments stemming from different fragmentations. In this paper we extend the data replication problem (DRP) by not only considering hard constraints to ensure a fixed replication factor but also adding soft constraints that express desired data locality of fragments. We furthermore analyze the case that there are more fragmentations (leading to the situation that some replication conditions are optional); and we study the influences of data updates (insertions and deletions) on the data distribution.

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“…In [6], the authors have proposed a better strategy of grouping the sites for allocation of fragments. A more recent work on fragmentation based on clustering is given in [12] which fragments the relation based on clustering.…”

Section: Introductionmentioning

confidence: 99%

Hybridized fragmentation of very large databases using clustering

Harikumar

Ramachandran

2015

2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

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Due to the ever growing needs of managing huge volume of data, together with the desire for consistent, scalable, reliable and efficient retrieval of information, an intelligent mechanism to design the storage structure for distributing the databases has become inevitable. The two critical facets of distributed databases are data fragmentation and allocation. Existing fragmentation techniques are based on the frequency and type of the queries as well as the statistics of the empirical data. However, very limited work is done to fragment the data based on the pattern of the tuples and the attributes responsible for such patterns. This paper presents a unique approach towards hybridized fragmentation, by applying subspace clustering algorithm, to come up with a set of fragments which partitions the data with respect to tuples as well as attributes. Projected clustering is the one that determines the clusters in the subspaces of high dimensional data. This concept leads to find the closely correlated attributes for different sets of instances thereby giving good hybridized fragments for distributed databases. Experimental results show that fragmenting the database based on clustering, results in reduced database access time as compared to the fragments chosen at design time using certain statistics.

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Clustering-based fragmentation and data replication for flexible query answering in distributed databases

Cited by 19 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 Replication Scheme for Multiple Fragmentations with Overlapping Fragments

Hybridized fragmentation of very large databases using clustering

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