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also aggregate data for query processing and the siz

  • AggregateQuery Processing in Data Semantic Scholar

    AggregateQuery. Processing in Data Warehousing. Environments*. Ashish Gupta. Venky Harinarayan. Dallan Quass. IBM Almaden Research Center. Abstract . also present rules for coalescing multiple aggregation operators in a query tree into a .. the size of intermediate relations when there are many duplie tuples 

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  • A Framework for Parallel Processing of Aggregate and mediaTUM

    eral concepts of parallel relational database query processing ([1], [2], [3], [26]) and objectrelational query processing ([15], [6]). of the UDF argument's storage size that is processed at the average call should be specified. If a UDF is data types in an ORDBMS typically some new aggregate functions will also be needed.

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  • RPKtable based efficient algorithm for joinaggregate query on

    However, it is timeconsuming to process joinaggregate query in big data environment, especially on MapReduce framework. The main The overall response time increases when the data size scales up. Consider Also this approach cannot assure the efficiency of adhoc query processing for dataintensive appliion.

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  • A Histogram based Analytical Approximate Query Processing for

    Sep 3, 2013 query processing over enormous amounts of data (spanning terabytes or even petabytes in size) to store their data in SQLbased aggregate queries, as well as response time or error bound constraint. . For the unknown parameters θ, in addition to get its point estimation, we also want to estimate a.

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  • CryptDB: protecting confidentiality with encrypted query processing

    of CryptDB on six real appliions (including phpBB, the HotCRP conference the DBMS server to perform query processing on encrypted data as it would on an . CryptDB also equips the server with CryptDBspecific userdefined functions (UDFs) that enable the server to compute on ciphertexts for certain operations.

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  • Processing Complex Aggregate Queries over Data Streams

    Our stream queryprocessing engine is also allowed a certain amount of memory, typically significantly smaller than the total size of the data set(s). This memory is used to maintain a concise and accurate synopsis of each data stream Ri, denoted by S(Ri). The key constraints imposed on each synopsis S(Ri) are that (1) it.

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  • Efficient OLAP Query Processing in Distributed Data Warehouses

    as OLAP queries, including SQL grouping/aggregation, data cubes [12], using marginal distributions our strategies for distributed OLAP query processing, and also quantify the performance benefits of our .. the fact relation in the database. Q depends only on the size of the base values relation and the aggregates to be.

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  • Dynamic Sample Selection for Approximate Query Processing

    ration process. While keeping query response times short is very important in many data mining and decision support appliions, exactness in query results is .. pling because it targets the most common type of analysis queries, aggregation queries with "groupbys". It also ex emplifies the flexibility of dynamic sample 

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  • Approximate Query Processing: No Silver Bullet Microsoft

    May 19, 2017 tegrating Approximate Query Processing into data platforms. 1. .. in a weaker sense (e.g., when input size is large) because they can sample of the input as data evolves is also challenging. Some works address the physical design is sues [55, 56, 76]. Next, online aggregation needs special opera.

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  • Query Processing in Sensor Networks

    Client PCBase station. Queries. Data. Distributed in network query processor. 3. TinyDB or Cougar. Wireless sensor network. Query input. Result collection. 2 . TinyDB also includes a mechanism for userdefined aggregates and a metadata management system that supports optimizations over them. Aggregation is a 

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  • Approximate Query Processing: Taming the Terabytes

    Aggregate queries: precision to "last decimal" not needed synopses of the data. Speedup obtained because synopses are orders of magnitude smaller than the original data. Approximate Query Processing: Taming the Terabytes Garofalakis, . Maintenance: Split & Merge approach as with equidepth, but must also.

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  • BlinkDB: Queries with Bounded Errors and Bounded Response

    It allows users to tradeoff query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas: (1) An adaptive optimization framework that builds and maintains a set of 

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  • CPU Performance Tradeoffs in BlockOriented Query Processing

    Figure 1: Diagram of the accesstime optimized. NSM data organization during computation of TPC. H Query 1 of selected offsets, called the selection vector. . a SUM aggregate of a 4byte integer column is computed repeatedly in a loop over a fixed dataset. The size of the data differs, to simulate different block sizes, 

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  • AggregateQuery Processing in Data Warehousing Brown CS

    AggregateQuery Processing in Data Warehousing. Environments* aggregated. Because the tables are large, better opti mization of aggregate queries has the potential to re sult in huge performance gains. Unfortunately, aggre gation operators a query tree. We also present rules for coalescing multiple aggregation.

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  • A system of metadata to control the process of query, aggregating

    A system of metadata to control the process of query, aggregating, cleaning and analysing large datasets of primary care data. We added six other elements to describe data: (1) the study or audit name (2) the query used to extract the data (3) the data collection number (4) the type of data, including specifying the units 

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  • AggregateQuery Processing in Data Semantic Scholar

    AggregateQuery. Processing in Data Warehousing. Environments*. Ashish Gupta. Venky Harinarayan. Dallan Quass. IBM Almaden Research Center. Abstract . also present rules for coalescing multiple aggregation operators in a query tree into a .. the size of intermediate relations when there are many duplie tuples 

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  • An Overview of Query Optimization in Relational Stanford InfoLab

    The cost model assigns an estimated cost to any partial or complete plan in the search space. It also determines the estimated size of the data stream for output of every operator in the plan. It relies on: (a) A set of statistics maintained on relations and indexes, e.g., number of data pages in a relation, number of pages in an.

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  • A Histogram based Analytical Approximate Query Processing for

    Sep 3, 2013 query processing over enormous amounts of data (spanning terabytes or even petabytes in size) to store their data in SQLbased aggregate queries, as well as response time or error bound constraint. . For the unknown parameters θ, in addition to get its point estimation, we also want to estimate a.

    Online
  • Multisource Skyline Query Processing in Road Networks UQ ITEE

    multisource skyline query in road networks but also the first effort to bor query processing, known as aggregate nearest neighbor the intermediate nodes visited, together with their network distances to the query point. The performance data reported in this section are the average of ten tests. 6.2 Candidate Size.

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  • Minimizing Communiion Cost in Distributed Multiquery Processing

    data movement cost. The emergence of largescale monitoring infrastructures such as wireless sensor networks poses similar distributed query processing challenges the queries must be processed . that the aggregate size is constant as well. In contrast We are also given a set of queries, Q1,, Qm, with the query Qi 

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  • An Overview of Query Optimization in Relational Systems Microsoft

    The cost model assigns an estimated cost to any partial or complete plan in the search space. It also determines the estimated size of the data stream for output of every operator in the plan. It relies on: (a) A set of statistics maintained on relations and indexes, e.g., number of data pages in a relation, number of pages in an.

    Online
  • Approximate Query Processing: No Silver Bullet Microsoft

    May 19, 2017 tegrating Approximate Query Processing into data platforms. 1. .. in a weaker sense (e.g., when input size is large) because they can sample of the input as data evolves is also challenging. Some works address the physical design is sues [55, 56, 76]. Next, online aggregation needs special opera.

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  • A SampleandClean Framework for Fast and Accurate Query

    We derive confidence intervals as a function of sample size and show samples of the data to process aggregate queries (e.g., avg, count we may execute queries on a sample of the data to reduce query times. (2) Data error: since realworld data is dirty, queries on the dirty data also lead to inaccurate query re sults.

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  • Query Processing in Sensor Networks

    Client PCBase station. Queries. Data. Distributed in network query processor. 3. TinyDB or Cougar. Wireless sensor network. Query input. Result collection. 2 . TinyDB also includes a mechanism for userdefined aggregates and a metadata management system that supports optimizations over them. Aggregation is a 

    Online
  • Building Fast and Reliable Approximate Query Processing Systems

    different aggregation function, like a MapReduce job in EARL [23]. When the size of the dataset (D) is too large or when the user prefers lower latency than the system can deliver, it is possible to save on I/O and computation by processing the query on less data, resorting to approximation. In particular, sampling has 

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  • Building Fast and Reliable Approximate Query Processing Systems

    different aggregation function, like a MapReduce job in EARL [23]. When the size of the dataset (D) is too large or when the user prefers lower latency than the system can deliver, it is possible to save on I/O and computation by processing the query on less data, resorting to approximation. In particular, sampling has 

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  • A Framework for Fast Approximate Query Processing on Temporal

    JMLR: Workshop and Conference Proceedings 36:2945, 2014. BIGMINE 2014. FAQ: A Framework for Fast Approximate Query Processing on Temporal Data. Udayan Khurana . This work also includes a discussion approximate aggregate and distance query processing we approach this problem by compos.

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  • An Overview of Query Optimization in Relational Systems Microsoft

    The cost model assigns an estimated cost to any partial or complete plan in the search space. It also determines the estimated size of the data stream for output of every operator in the plan. It relies on: (a) A set of statistics maintained on relations and indexes, e.g., number of data pages in a relation, number of pages in an.

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  • metadata challenge for query processing over arXiv

    through the base station to get aggregated data from the nodes. perform aggregation of data for a particular query. [20] Each node in TinyDB maintains a alog of metadata that describes its local attributes, events, and userdefined support innetwork query processing metadata at sensor node is also required.

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  • Approximate Query Processing: Taming the Terabytes

    Aggregate queries: precision to "last decimal" not needed synopses of the data. Speedup obtained because synopses are orders of magnitude smaller than the original data. Approximate Query Processing: Taming the Terabytes Garofalakis, . Maintenance: Split & Merge approach as with equidepth, but must also.

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