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Optimized Stratified Sampling for Approximate Query

Optimized Stratified Sampling for Approximate Query Processing SURAJIT CHAUDHURI Microsoft Research GAUTAM DAS University of Texas at Arlington and VIVEK NARASAYYA Microsoft Research The ability to approximately answer aggregation queries accurately and efficiently is of great benefit for decision support and data mining tools.

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ACM Transactions on Database Systems 2007Surajit Chaudhuri Gautam Das Vivek R Narasayya Microsoft University of Texas at ArlingtonDecision support system Information extraction Mathematical optimization Algorithm

aggregate query definition English Glosbe

Improved techniques are disclosed for processing data stream queries wherein a data stream is obtained, a set of aggregate queries to be executed on the data stream is obtained, and a query plan for executing the set of aggregate queries on the data stream is generated.

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Efficient Processing of Ad-Hoc Top-k Aggregate Queries in

Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP Chengkai Li1 Kevin Chen-Chuan Chang1 Ihab F. Ilyas2 ficient processing of ad-hoc top-k aggregate queries. We define a cost metric on retrieved tuples, capturing our goal of producing only necessary tuples for generating top k

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Chengkai Li Kevin Chenchuan Chang Ihab F Ilyas University of Illinois at Urbana Champaign University of WaterlooDatabase

OLAP Online Analytical Processing OLAP cubes

Online Analytical Processing (OLAP) OLAP faster analyzing data If users ask often for a specific combination of measures and dimensions which are not yet stored in an aggregate, define an extra aggregate for that combination. Next time the same query has to be answered the appliance detects that and retrieves the data simply from

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Online transaction processing (OLTP) Microsoft Docs

Analytics against the data, that rely on aggregate calculations over millions of individual transactions, are very resource intensive for an OLTP system. the queries tend to be complex, because most queries need to de-normalize the data by using joins. For more information, see Online Analytical Processing (OLAP). In Azure, all of the

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Accuracy vs. Lifetime Linear Sketches for Aggregate

Accuracy vs. Lifetime Linear Sketches for Aggregate (OLAP) and query processing over streams. Con-straints imposed by streams (see Babu and Widom ) make techniques for stream computations suitable for sensors. We adopt a summary based approximate query processing approach for sensor networks as illustrated in Fig. 1. During each round,

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Avoiding SQL anti-patterns BigQuery Google Cloud

Google Cloud Platform Overview Use an approximate aggregate function such as APPROX_TOP_COUNT to determine if the data is skewed. system. BigQuery focuses on Online Analytical Processing (OLAP) by using table scans and not point lookups. If you need OLTP-like behavior (single-row updates or inserts)

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Accuracy Vs. Lifetime Linear Sketches for Approximate

Lifetime Linear Sketches for Approximate Aggregate Range Queries in Sensor Networks and use themto answer aggregate queries in an approximate manner at the base{station. There is a trade{o between (OLAP) and query processing over streams. The idea is to

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Revisiting Reuse for Approximate Query Processing VLDB

Revisiting Reuse for Approximate Query Processing stead rely upon approximate query processing (AQP) tech-niques, which provide query result estimates with bounded errors. Many AQP systems use some form of biased sam- cutes an aggregate query using this formulation (Section 2.2),

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Approximate Selection Queries over Imprecise Data

Approximate Selection Queries over Imprecise Data Iosif Lazaridis swers, rather than the approximate aggregate queries over numerical data examined in the literature; second, by aim- replicating them at the query processing site precisely.

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international conference on data engineering 2004Iosif Lazaridis Sharad Mehrotra University of California IrvineData processing Database

Fast Approximate Evaluation of OLAP Queries for Integrated

Fast Approximate Evaluation of OLAP Queries for Integrated Statistical Data Jose Luis Ambite 1, Cyrus Shahabi 2, evaluation of OLAP (On-Line Analytical Processing) queries on our integrated data. In the first place we we provide a quick approximate query response that becomes increasingly accurate

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Oracle Essbase Aggregate Storage Option Benchmark on

leading online analytical processing (OLAP) server available today. Oracle Essbase scripts against a tuned Essbase database Average query response time 1 second Dimension Build Loading 13 million members to the Oracle Essbase Aggregate Storage Option Benchmark 9

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Approximating multi-dimensional aggregate range queries

Alfredo Cuzzocrea, Providing probabilistically-bounded approximate answers to non-holistic aggregate range queries in OLAP, Proceedings of the 8th ACM international workshop on Data warehousing and OLAP, November 04-05, 2005, Bremen, Germany

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Approximate Range Queries by Histograms in OLAP

Approximate Range Queries by Histograms in OLAP 10.4018/978-1-59140-557-3.ch010 Online analytical processing applications typically analyze a large amount of data by means of repetitive queries involving aggregate measures on such data.

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What is OLAP? Database.Guide

Oct 11, 2017OLAP (Online Analytical Processing) Larger Queries. OLAP applications usually have a smaller number of users than OLTP applications. (such as an individual's bank account). OLAP on the other hand, is usually concerned with complex aggregate queries across large data sets (such as querying all customers' bank accounts).

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On-Line Application Processing Stanford University

OLAP On-Line Application Processing (OLAP, or "analytic") queries are, typically Few, but complex queries-- may run for hours. Queries do not depend on having an absolutely up-to-date database.

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New Sampling-Based Estimators for OLAP Queries

New Sampling-Based Estimators for OLAP Queries One important way in which sampling for approximate query processing in a database environment differs from tra- attached to the aggregate query. For example, imagine we have a database table COMPLAINTS(PROF,SEMESTER,

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international conference on data engineering 2006Ruoming Jin Leonid Glimcher Chris Jermaine Gagan Agrawal Kent State University Ohio State University University of FloridaComputer hardware Histogram Sampling Sociology Statistics Relational database

Data Warehousing and OLAP Flashcards Quizlet

- support information processing by providing a solid platform of consolidated, historical data for analysis Tuned for OLAP, complex OLAP queries, multidimensional view, consodilation. if the result derived by applying the function to n aggregate values is the same as that derived by applying the function on all the data without partioning.

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Approximate Query Processing Overview and Challenges

Approximate Query Processing (APQ) Data Synopsis Data stream Query Query Exact answer (slow) Approx. answer OLAP Workloads TPC-H TPC-DS Facebook Conviva Inc. Customer System ABM QuickR Arbitrary SQL aggregate queries Subqueries [Joshi and

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aggregate query processing definition saneringchemiepack

processing aggregation queries over encryptedproject hosting, efficient query processing directly on the encrypted database. . define six aggregate query types, and, for each query type, present and analyze queryaggregate-query processing in data warehousing environments, aggregate-query processing in data warehousing aggregate queries

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FAST RANGE AGGREGATE QUERIES FOR BIG DATA ANALYSIS

FAST RANGE AGGREGATE QUERIES FOR BIG DATA ANALYSIS M.R.ABHIMAN RAAM1, V.S.ARAVINDAKSHAN2, P.A.HARISH3, Aggregation which provides progressive approximate aggregate answers for both single table and multiple joined tables. 3. PROPOSED SYSTEM queries in OLAP data cubes," ACM SIGMOD Rec., vol. 26, no. 2, pp. 7388, 1997.

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Efficient Processing of Ad-Hoc Top-k Aggregate Queries in

Efficient Processing of Ad-Hoc Top-k Aggregate Queries in OLAP Chengkai Li1 Kevin Chen-Chuan Chang1 Ihab F. Ilyas2 cient processing of ad-hoc top-k aggregate queries. We dene a cost metric on retrieved tuples, capturing our goal of producing only necessary tuples for generating top k

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The framework for approximate queries on simulation data

The framework for approximate queries on simulation data. The system uses the state of the art in approximate query processing techniques to build a novel framework for progressive data analysis. S. Mehrota, Progressive approximate aggregate queries with a multi-resolution tree structure, in Proc. ACM SIGMOD Int. Conf. Management of

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