Efficient top-k retrieval for user preference queries

Dabringer, Claus; Eder, Johann

doi:10.1145/1982185.1982414

Cited by 7 publications

(2 citation statements)

References 19 publications

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“…In the small data, the elements which meet the conditions, need be filtrated and sorted. Then the top-K [22][23][24] elements are selected and returned. in order to obtain top-K data rapidly, using binary location method, after traversal of the local data, the elements which meet the conditions, can be add to the sorted sequence.…”

Section: Definition: Basic Datablock Intermediate Datablock and Resumentioning

confidence: 99%

A Real-Time Distributed Query Solution Based on the OPC

Yang¹,

Ju²,

Zhang³

et al. 2014

2014 International Conference on Cloud Computing and Big Data

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The distributed query is one of the research focus in the Big Data. Nowadays, many companies and institutions provide technology and products to realize function or improve efficiency in the all kinds of database. In the scene of electricity, using these techniques, the real-time requirement (<10s) cannot be met. This paper provides a real-time distributed query solution with Objectification Parallel Computing (OPC) to solve the above challenges. The data split from Big Data, is distributed stored in memory of cluster in the OPC. In the solution, making use of the thought of divide and rule and tree merging, there are two stages. The first stage is local data query. The intermediate query result can be obtained. The second stage is multistage summarizing. The final result can be returned to user. The solution has been applied to the power production management system (PMS) of State Grid of China. The results show that solution is efficiently reliable and meets real-time.

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Section: Definition: Basic Datablock Intermediate Datablock and Resumentioning

confidence: 99%

A Real-Time Distributed Query Solution Based on the OPC

Yang¹,

Ju²,

Zhang³

et al. 2014

2014 International Conference on Cloud Computing and Big Data

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show abstract

“…Top-k queries are also popular for providing users ranked search results they are used from web search engines. Top-k queries, in particular, the optimization of top-k query processing for central databases received a lot of attention [3,5,8,9,18,19,20,21,23,26]. Optimizing top-k queries in distributed environment, in particular in highly distributed networks of federated or peer to peer databases still has significant research needs.…”

Section: Introductionmentioning

confidence: 99%

Towards Adaptive Distributed Top-k Query Processing

Dabringer

Eder

2016

Communications in Computer and Information Science

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ADiT is an adaptive approach for processing distributed topk queries over peer-to-peer networks optimizing both system load and query response time. This approach considers the size of the peer to peer network, the amount k of searched objects, the network capabilities of a connected peer, i.e. the transmission rate, the amount of objects stored on each peer, and the speed of a peer in processing a local topk query. In extensive experiments with a variety of scenarios we could show that ADiT outperforms state of the art distributed query processing techniques.

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Fast Top-Q and Top-K Query Answering

Dabringer

Eder

2017

Future Data and Security Engineering

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Efficient top-k retrieval for user preference queries

Cited by 7 publications

References 19 publications

A Real-Time Distributed Query Solution Based on the OPC

A Real-Time Distributed Query Solution Based on the OPC

Towards Adaptive Distributed Top-k Query Processing

Fast Top-Q and Top-K Query Answering

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