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Keywords Sampling Approximate Query Processing Data Warehousing Abstract Decision support queries usually involve accessing enormous amount of data requiring significant retrieval time Faster retrieval of query results can often save precious time for the decision maker Pre computation of materialised views and sampling are two ways of achieving significant speed up However drawing
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Aggregates are used in dimensional models of the data warehouse to produce dramatic positive effects on the time it takes to query large sets of the simplest form an aggregate is a simple summary table that can be derived by performing a Group by SQL query
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A data warehouse is a relational database that aggregates structured data from across an entire organization It pulls together data from multiple sources—much of it is typically online transaction processing OLTP data The data warehouse selects organizes and aggregates data for efficient comparison and analysis
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Existing aggregate query processing algorithms focus on optimizing various query operations but give less importance to communication cost overhead Two phase algorithm However in cloud architectures the communi cation cost overhead is an important factor in query processing
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aggregate query processing definition Mining World Quarry AggregateQuery Processing in Data Warehousing CiteSeer Section 2 motivates our work with an AggregateQuery Processing in Data Warehousing AggregateQuery Processing in Data Warehousing how an aggregate query tree can be The tree corresponding to the Oline Chat
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Home Conferences VLDB Proceedings VLDB 95 Aggregate Query Processing in Data Warehousing Environments Article Free Access Share on Aggregate Query Processing in Data Warehousing Environments Authors Ashish Gupta
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Aggregate Query Processing in Data Warehousing Environments In this paper we introduce generalized projections GPs an extension of duplicateeliminating projections that capture aggregations groupbys duplicate eliminating projections distinct and duplicate preserving projections in a common uni ed framework Using GPs we extend well known and simple algorithms for SQL queries that
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Aggregate Query Processing in Data Warehousing Environments inproceedings{Gupta1995AggregateQueryPI title={Aggregate Query Processing in Data Warehousing Environments} author={Ashish Kumar Gupta and Venky Harinarayan and Dallan Quass} booktitle={VLDB} year={1995} } A Gupta Venky Harinarayan D Quass Published in VLDB 11 September 1995
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In order for this process to work database administrators must first formulate the desired query typically an aggregate SQL query after closely studying database catalogs Then the query is processed
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Aggregate and Grouping Functions in Object Oriented Databasesof aggregate queries and data structures can help in designing future information systems data mining data warehousing and statistical and The advantages of storing and processing data in databases instead of files has not been rec
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Christopher Adamson is a data warehousing consultant and founder of Oakton Software LLC An expert in star schema design he has managed and executed data warehouse implementations in a variety of industries His customers have included Fortune 500 companies large and small businesses government agencies and data warehousing …
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An AdTech company in the US provides processing payment and analytics services for digital advertisers Data processing and analytics drive their entire business So they needed a data warehouse that could keep up with the scale of modern big data systems but provide the semantics and query performance of a traditional relational database
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Apr 9 2022Data warehousing query tools Data warehousing query tools Open Aggregate Navigation Aggregate navigation is the ability to automatically choose pre stored summaries or aggregates in the course of processing a user s SQL requests Aggregate navigation must be performed silently and anonymously without the end user or the application
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Oracle is pushing Exadata 2 as being a great system for any of OLTP OnLine Transaction Processing data warehousing or presumably the integration of same This claim rests on a few premises namely Exadata is great for data warehousing At this time that s a claim much better supported by marketing and theory than by practice
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DBLife Aggregate Query Processing in Data Warehousing Aggregate Query Processing in Data Warehousing Environments Bing Google Google Scholar Yahoo Source VLDB Year 1995 Pages 358 369 Authors Ashish Gupta Venky Go to Product Center
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Our results show the effec OLAP tools for analyzing network trace data is tiveness of our strategies for distributed OLAP that the tools require all the data to be available in query processing and also quantify the perfor a single centralized data warehouse The inher mance benefits of our optimizations
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Aggregate Queries in Wireless Sensor Networks Hindawi Know More 26 Sep 2024 Recently as efficient processing of aggregate queries for fetching desired data from sensors has been recognized as a crucial part Optimizing Aggregate Query Processing in Cloud Data Warehouses Know More In this paper we study and optimize the
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Data Warehousing Technology n Aggregate targeting n n n Aggregates flow up from a wide selection of data and then Targeted decisions flow down Workload type n Broad aggregate queries over ranges of values n n Deep queries that require precise individualized information n n E g which frequent flyers have been delayed several times in the last month Dynamic vs Static queries that
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Quicker and more efficient query processing Reduced client processing loads and network traffic because aggregation work is shifted to servers Opportunities for caching aggregations because similar queries can leverage existing work An Aggregate Scenario To illustrate the use of the GROUP BY extension this chapter uses the sh data of the sample schema All the examples refer to data from
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A data warehouse is built to store large quantities of historical data and enable fast complex queries across all the data typically using Online Analytical Processing OLAP A database was built to store current transactions and enable fast access to specific transactions for ongoing business processes known as Online Transaction Processing OLTP
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The compute layers comprise multiple compute clusters with nodes processing queries in parallel Compute clusters are the sets of virtual machines grouped to perform computation tasks These clusters are sometimes called virtual warehouses In the storage layers data is organized in partitions to be further optimized and compressed The client layers are responsible for management activities
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This allows for faster aggregate queries in line with the type of queries you need to run in a data warehouse Another feature that drastically improves query speeds is massively parallel processing This is done by using many machines to coordinate query processing for large datasets
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A single source query will select the necessary source data filter out new records only and aggregate the facts Lookups are necessary only for surrogate keys that link to aggregate dimensions The only complication faced will be processing data that is aggregated over time Special care must be taken to properly accumulate data for the
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When dealing with such data dimensional models such as Data Warehouses can provide a more consistent and accessible form of data storage than Relational Databases A Data Warehouse can allow you to aggregate data from multiple sources into a single repository for analysis reporting and Business Intelligence
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A key design element for Data warehouses They are the values that we are interested in usually aggregate summary type calculation eg count sum average median eg total revenue avg profit no of sales Also known as measures Dependent variables
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The scheduling software requires an interface with the data warehouse which will need the scheduler to control overnight processing and the management of aggregations Disk Configuration − Disk configuration also needs to be tested to identify I/O bottlenecks The test should be performed with multiple times with different settings
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Aggregation in traditional database systems is performed in batch mode a query is submitted the system processes a large volume of data over a long period of time and eventually the final answer is returned This archaic approach is frustrating to users and has been abandoned in most other areas of computing
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Nguyen Trong Duc Approximate query processing in a data warehouse using random sampling 2024 Graduate Theses and Dissertations 18195 https ///etd/18195 This Dissertation is brought to you for free and open access by the Iowa State University Capstones Theses and Dissertations at Iowa State University Digital Repository It has been accepted for inclusion in Graduate
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Aggregate Query Processing in Data Warehousing Environments Ashish Gupta Venky Harinarayan Dallan Quass VLDB 1995 query processing Bypassing Joins in Disjunctive Queries Michael Steinbrunn Klaus Peithner Guido Moerkotte Alfons Kemper VLDB 1995 query optimization Coloring Away Communication in Parallel Query Optimization
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Optimizing Aggregate Query Processing in Cloud Data Warehouses and reducing database table access required for aggregate query Keywords Aggregate Operation Communication Cost Read Optimized Database Data Warehouse Cloud Storage Query Optimization 1 Introduction Data Warehouses or decision support systems use join group by and aggre gate operations very often in formulating analytical
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