On the propagation of errors in the size of join results

Ioannidis, Yannis; Christodoulakis, Stavros

doi:10.1145/119995.115835

Cited by 80 publications

(91 citation statements)

References 4 publications

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“…The results prove the accuracy of the formula, the relative error being 8% for binary joins and 38% for queries of four inputs. These numbers are comparable with previous work on selectivity of spatial selections [31] or joins [32], as well as, with error propagation experiments in the context of relational queries [13]. The proposed models are essential for the optimization of queries that involve several spatial logical operators, possibly in addition to some non-spatial ones.…”

Section: Discussionsupporting

confidence: 81%

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Complex Spatial Query Processing

2004

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The user of a Geographical Information System is not limited to conventional spatial selections and joins, but may also pose more complicated and descriptive queries. In this paper, we focus on the ef®cient processing and optimization of complex spatial queries that involve combinations of spatial selections and joins. Our contribution is manifold; we ®rst provide formulae that accurately estimate the selectivity of such queries. These formulae, paired with cost models for selections and joins can be used to combine spatial operators in an optimal way. Second, we propose algorithms that process spatial joins and selections simultaneously and are typically more ef®cient than combinations of simple operators. Finally we study the problem of optimizing complex spatial queries using these operators, by providing (i) cost models, and (ii) rules that reduce the optimization space signi®cantly. The accuracy of the selectivity models and the ef®ciency of the proposed algorithms are evaluated through experimentation.

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Section: Discussionsupporting

confidence: 81%

“…than the input data. However, this is an unavoidable problem, which also exists in query optimization of relational queries involving many operators [13].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Complex Spatial Query Processing

2004

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“…After several years of inactivity on the topic of histograms, interest in it was renewed in the context of studying how initial errors in statistics maintained by the database propagate in estimates of the size of complex query results [36]. In particular, it was shown that, under some rather general conditions, in the worst case, errors propagate exponentially in the query size (i.e., in the number of joins), removing any hope for high-quality estimates for large multi-join queries.…”

Section: Optimal Sort Parametermentioning

confidence: 99%

The History of Histograms (abridged)

Ioannidis

2003

Proceedings 2003 VLDB Conference

322

230

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The history of histograms is long and rich, full of detailed information in every step. It includes the course of histograms in different scientific fields, the successes and failures of histograms in approximating and compressing information, their adoption by industry, and solutions that have been given on a great variety of histogram-related problems. In this paper and in the same spirit of the histogram techniques themselves, we compress their entire history (including their "future history" as currently anticipated) in the given/fixed space budget, mostly recording details for the periods, events, and results with the highest (personally-biased) interest. In a limited set of experiments, the semantic distance between the compressed and the full form of the history was found relatively small! PrehistoryThe word 'histogram' is of Greek origin, as it is a composite of the words 'isto-s' (ιστ os) (= 'mast', also means 'web' but this is not relevant to this discussion) and 'gram-ma' (γραµµα) (= 'something written'). Hence, it should be interpreted as a form of writing consisting of 'masts', i.e., long shapes vertically standing, or something similar. It is not, however, a

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“…They outperform naïve query processing (NaiveQP) with a factor 450 and attribute statistics profiling without grouping (AttrSP) with a factor 50. This demonstrates that the grouped strategies GroupSP and 2PhaseSP alleviate the problem of errors in the cost estimates [12] by measuring real execution time and selectivity for each group. The difference between GroupSP and 2PhaseSP is insignificant (Fig.…”

Section: Resultsmentioning

confidence: 80%

Optimization and Execution of Complex Scientific Queries over Uncorrelated Experimental Data

Fomkin

Risch

2009

Lecture Notes in Computer Science

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Abstract. Scientific experiments produce large volumes of data represented as complex objects that describe independent events such as particle collisions. Scientific analyses can be expressed as queries selecting objects that satisfy complex local conditions over properties of each object. The conditions include joins, aggregate functions, and numerical computations. Traditional query processing where data is loaded into a database does not perform well, since it takes time and space to load and index data. Therefore, we developed SQISLE to efficiently process in one pass large queries selecting complex objects from sources. Our contributions include runtime query optimization strategies, which during query execution collect runtime query statistics, reoptimize the query using collected statistics, and dynamically switch optimization strategies. Furthermore, performance is improved by query rewrites, temporary view materializations, and compile time evaluation of query fragments. We demonstrate that queries in SQISLE perform close to hard-coded C++ implementations of the same analyses.

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On the propagation of errors in the size of join results

Cited by 80 publications

References 4 publications

Complex Spatial Query Processing

Complex Spatial Query Processing

The History of Histograms (abridged)

Optimization and Execution of Complex Scientific Queries over Uncorrelated Experimental Data

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