Efficient and scalable statistics gathering for large databases in Oracle 11g

Chakkappen, Sunil; Cruanes, Thierry; Dageville, Benoît; Jiang, Linan; Shaft, Uri; Su, Hong; Zaït, Mohamed

doi:10.1145/1376616.1376721

Cited by 19 publications

(7 citation statements)

References 17 publications

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“…One-dimensional histograms have been used widely for selectivity estimation in databases [30,22]. Oracle provides effective statistics collection techniques for large singledimensional data tables using the one-pass distinct sampling method [13]. However, multi-dimensional histograms, such as those required for spatial data, pose an entirely different challenge [21].…”

Section: Related Workmentioning

confidence: 99%

“…Statistics collection has been studied for building histograms for query optimization purposes. Sampling is used extensively for conventional data [27,20,19,15,6,13] but has been shown to be ineffective in the spatial domain [9,8,7]. Sampling is based on the assumption that a small subset of data can represent the overall data distribution.…”

Section: Introductionmentioning

confidence: 99%

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Statistics collection in oracle spatial and graph

Bamba

Ravada

et al. 2013

Proc. VLDB Endow.

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Oracle Spatial and Graph is a geographic information system (GIS) which provides users the ability to store spatial data alongside conventional data in Oracle. As a result of the coexistence of spatial and other data, we observe a trend towards users performing increasingly complex queries which involve spatial as well as non-spatial predicates. Accurate selectivity values, especially for queries with multiple predicates requiring joins among numerous tables, are essential for the database optimizer to determine a good execution plan. For queries involving spatial predicates, this requires that reasonably accurate statistics collection has been performed on the spatial data. For extensible data cartridges such as Oracle Spatial and Graph, the optimizer expects to receive accurate predicate selectivity and cost values from functions implemented within the data cartridge. Although statistics collection for spatial data has been researched in academia for a few years; to the best of our knowledge, this is the first work to present spatial statistics collection implementation details for a commercial GIS database. In this paper, we describe our experiences with implementation of statistics collection methods for complex geometry objects within Oracle Spatial and Graph. Firstly, we exemplify issues with previous partitioning-based algorithms in presence of complex geometry objects and suggest enhancements which resolve the issues. Secondly, we propose a main memory implementation which not only speeds up the disk-based partitioning algorithms but also utilizes existing R-tree indexes to provide surprisingly accurate selectivity estimates. Last but not the least, we provide extensive experimental results and an example study which displays the efficacy of our approach on Oracle query performance.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistics collection in oracle spatial and graph

Bamba

Ravada

et al. 2013

Proc. VLDB Endow.

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“…In order to improve the efficiency of the SQL-based approach for evaluating graph queries, we follow the idea of automatic creation and usage of higher-level data about data (metadata) [32][33][34] , here referred to as graph features knowledge (GFK ). We construct a layer of summary data which consists of three main components: graph descriptors, aggregate graph descriptors and Nodes-Edges Markov Summaries.…”

Section: Graph Features Knowledgementioning

confidence: 99%

Efficient Relational Techniques for Processing Graph Queries

Sakr

Al‐Naymat

2010

J. Comput. Sci. Technol.

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Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions and semantic web. To effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. For example, Subgraph and Supergraph queries are important types of graph queries which have many applications in practice. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. Relational database management systems (RDBMSs) have repeatedly been shown to be able to efficiently host different types of data such as complex objects and XML data. RDBMSs derive much of their performance from sophisticated optimizer components which make use of physical properties that are specific to the relational model such as sortedness, proper join ordering and powerful indexing mechanisms. In this article, we study the problem of indexing and querying graph databases using the relational infrastructure. We present a purely relational framework for processing graph queries. This framework relies on building a layer of graph features knowledge which capture metadata and summary features of the underlying graph database. We describe different querying mechanisms which make use of the layer of graph features knowledge to achieve scalable performance for processing graph queries. Finally, we conduct an extensive set of experiments on real and synthetic datasets to demonstrate the efficiency and the scalability of our techniques.

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“…In order to improve the efficiency of the SQL-based approach for evaluating supergraph queries, we employ the idea of automatic creation and usage of higher-level data about data (metadata) [3,6], hereafter referred to as graph features knowledge. This information is used during the query optimization and execution phases to improve the performance of query evaluation.…”

Section: Graph Features Knowledgementioning

confidence: 99%

An efficient features-based processing technique for supergraph queries

Sakr

Al‐Naymat²

2010

Proceedings of the Fourteenth International Database Engineering &Amp; Applications Symposium on - IDEAS '10

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Graphs are widely used for modeling complicated data such as social networks, chemical compounds, protein interactions, XML documents and multimedia databases. To be able to effectively understand and utilize any collection of graphs, a graph database that efficiently supports elementary querying mechanisms is crucially required. Supergraph query is an important type of graph queries which has many practical applications. Given a graph database D, the answer set of a supergraph query q is computed by retrieving all graphs in D which are fully contained in q. A primary challenge in computing the answers of graph queries is that pair-wise comparisons of graphs are usually hard problems. For example, subgraph isomorphism is known to be NPcomplete. Clearly, the success of any graph database application is directly dependent on the efficiency of the graph indexing and query processing mechanisms. In this paper, we study the problem of using the relational infrastructure to achieve an efficient evaluation of supergraph queries. We rely on an effective and efficient layer of features-based summary structures, called graph features knowledge, to reduce the required number of pair-wise graph comparisons and boost the efficiency of query processing. Finally, we conduct an extensive set of experiments on real and synthetic data sets to demonstrate the efficiency and the scalability of our approach.

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Efficient and scalable statistics gathering for large databases in Oracle 11g

Cited by 19 publications

References 17 publications

Statistics collection in oracle spatial and graph

Statistics collection in oracle spatial and graph

Efficient Relational Techniques for Processing Graph Queries

An efficient features-based processing technique for supergraph queries

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