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Distributed Identification of Top-l Inner Product Elements and its Application in a Peer-to-Peer Network

Metadata Updated: December 7, 2023

Inner product computation is an important primitive used in many techniques for feature dependency detection, distance computation, clustering and correlation computation among others. Recently, peer-to-peer networks are getting increasing attention in various applications involving distributed file sharing, sensor networks, and mobile ad hoc networks. Efficient identification of top few inner product entries from the entire inner product matrix of features in a distributed peer-to-peer network is a challenging problem since centralizing the data from all the nodes in a synchronous, communication efficient manner may not be an option. This paper deals with the problem of identifying significant inner products among features in a peer-to-peer environment where different nodes observe a different set of data. It uses an ordinal framework to develop probabilistic algorithms to find top-LaTeX elements in the inner product matrix. These l inner product entries are important in making crucial decisions about dependency or relatedness between feature pairs, important for a number of data mining applications. In this paper we present experimental results demonstrating accurate and scalable performance of this algorithm for large peer-to-peer networks and also describe a real-world application for this algorithm.

Access & Use Information

Public: This dataset is intended for public access and use. License: No license information was provided. If this work was prepared by an officer or employee of the United States government as part of that person's official duties it is considered a U.S. Government Work.

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Dates

Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Data Update Frequency irregular

Metadata Source

Harvested from NASA Data.json

Additional Metadata

Resource Type Dataset
Metadata Created Date November 12, 2020
Metadata Updated Date December 7, 2023
Publisher Dashlink
Maintainer
Identifier DASHLINK_177
Data First Published 2010-09-22
Data Last Modified 2020-01-29
Public Access Level public
Data Update Frequency irregular
Bureau Code 026:00
Metadata Context https://project-open-data.cio.gov/v1.1/schema/catalog.jsonld
Metadata Catalog ID https://data.nasa.gov/data.json
Schema Version https://project-open-data.cio.gov/v1.1/schema
Catalog Describedby https://project-open-data.cio.gov/v1.1/schema/catalog.json
Harvest Object Id 79206132-38bf-4239-aa61-38fc2c1ebab7
Harvest Source Id 58f92550-7a01-4f00-b1b2-8dc953bd598f
Harvest Source Title NASA Data.json
Homepage URL https://c3.nasa.gov/dashlink/resources/177/
Program Code 026:029
Source Datajson Identifier True
Source Hash 366d77047a56dc7aaec080b129a823c55ac39150cdaff784ad7cb6269cbcba92
Source Schema Version 1.1

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