Efficient Processing of Time-Joins in Temporal Data Bases

“…Their main focus is on optimizing temporal joins. [45] focuses on processing nestedloops, [21] and [32] focus on processing sort-merge, whereas [19] proposes three basic join algorithms (nestedloop, sort-merge, and partition-based) for valid-time databases. [55] and [54] focus on joining data partitioned by their valid time intervals.…”

Section: Related Workmentioning

confidence: 99%

Multiversion join index for multiversion data warehouse

Chmiel

Morzy

Wrembel

2009

Information and Software Technology

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“…3 shows a sort-merge algorithm for a temporal intersection join. Originally, it was discussed as Algorithm 2 in [Rana and Fotouhi, 1993] which is similar to algorithm TI-I in [Gunadhi and Segev, 19911. The two if-statements in the inner loop check the two situations in which no intersection occurs:…”

Section: Sort-merge Joinsmentioning

confidence: 99%

Optimisation of partitioned temporal joins

Zurek

1997

Lecture Notes in Computer Science

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Joins are the most expensive and performance-critical operations in relational database systems. In this thesis, we investigate processing techniques for joins that are based on a temporal intersection condition. Intuitively, such joins are used whenever one wants to match data from two or more relations that is valid at the same time. This work is divided into two parts. First, we analyse techniques that have been proposed for equi-joins. Some of them have already been adapted for temporal join processing by other authors. However, hash-based and parallel techniques-which are usually the most efficient ones in the context of equijoins-have only found little attraction and leave several temporal-specific issues unresolved. Hash-based and parallel techniques are based on explicit symmetric partitioning. In the case of an equi-join condition, partitioning can guarantee that the relations are split into disjoint fragments; in the case of a temporal intersection condition, partitioning usually results in non-disjoint fragments with a large number of tuples being replicated between fragments. This causes a considerable overhead for partitioned temporal join processing. This problem is an instance of the 'min-max dilemma': minimising the number of replicated tuples means minimising the number of fragments, thus minimising the degree of parallelism-however, increasing the number of fragments and therefore the degree of parallelism also increases the number of tuple replications. We analyse this problem and show that there is an algorithm of polynomial time complexity that computes an optimal solution for the interval partitioning problem (IP). This result concludes the analytical part. In the second, the synthetical part of this work, we focus on the conclusions that can be drawn from the results of the first part. We propose and develop an optimisation process that • analyses the temporal relations that participate in a temporal join, • proposes several possible partitions for these relations, • analyses these partitions and predicts their performance implications on the basis of a parameterised cost model, and. chooses the cheapest partition to process the temporal join. We also show how this process can be efficiently implemented by using a new index structure, called the IP-table. The thesis is concluded by a thorough experimental evaluation of the optimisation process and a chapter that shows the suitability of IP-tables in a wider context of temporal query optimisation, namely using them to estimate selectivities of temporal join conditions. After over 1000 days of PhD research, around 500 pages of thesis, paper and report writing, I can finally add the final and the most enjoyable part: it is these lines in which I can thank the many people that have enabled me to produce this work by providing the fruitful environment that I have had in the last three years. First of all, I have to thank Isabel, my wife, for her endless patience and support and for cheering me up when things did not run as smoothly as my cabeza ...

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“…[22] assumed that the smaller relation fits in memory and proposed seven nested-loop T-Join algorithms. [9] provided sort-merge T-Join and TE-Join algorithms when one or both relations are sorted.…”

Section: Previous Workmentioning

confidence: 99%

“…Previous temporal join research has focused on nonindexed joins [9,16,22,26,23,24,29]. The common approach is to scan both relations and select the records that need to be joined.…”

Section: Introductionmentioning

confidence: 99%

Efficient temporal join processing using indices

Zhang

Tsotras

Seeger

Proceedings 18th International Conference on Data Engineering

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We examine the problem of processing temporal joins in the presence of indexing schemes. Previous work on temporal joins has concentrated on non-indexed relations which were fully scanned. Given the large data volumes created by the ever increasing time dimension, sequential scanning is prohibitive. This is especially true when the temporal join involves only parts of the joining relations (e.g., a given time interval instead of the whole timeline). Utilizing an index becomes then beneficial as it directs the join to the data of interest. We consider temporal join algorithms for three representative indexing schemes, namely a B+-tree, an R*-tree and a temporal index, the Multiversion B+-tree (MVBT). Both the B+-tree and R*-tree result in simple but not efficient join algorithms because neither index achieves good temporal data clustering. Better clustering is maintained by the MVBT through record copying. Nevertheless, copies can greatly affect the correctness and effectiveness of the join algorithms. We identify these problems and propose efficient solutions and optimizations. An extensive comparison of all index based temporal joins, using a variety of datasets and query characteristics shows that the MVBT based join algorithms are consistently faster. In particular the link-based algorithm has the most robust behavior. In our experiments it showed a ten-fold improvement over the R*-tree joins while it was between six and thirty times faster than the B+-tree joins.

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Efficient Processing of Time-Joins in Temporal Data Bases

Cited by 10 publications

References 0 publications

Multiversion join index for multiversion data warehouse

Multiversion join index for multiversion data warehouse

Optimisation of partitioned temporal joins

Efficient temporal join processing using indices

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