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Better Streaming Algorithms for the Maximum Coverage Problem
We study the classic NP-Hard problem of finding the maximum k -set coverage in the data stream model: given a set system of m sets that are subsets of a universe { 1 , ... , n } , find the k sets that cover the most number of distinct elements. The problem can be approximated up to a factor 1 − 1 /...
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Published in: | Theory of computing systems 2019-10, Vol.63 (7), p.1595-1619 |
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creator | McGregor, Andrew Vu, Hoa T. |
description | We study the classic NP-Hard problem of finding the maximum
k
-set coverage in the data stream model: given a set system of
m
sets that are subsets of a universe
{
1
,
...
,
n
}
, find the
k
sets that cover the most number of distinct elements. The problem can be approximated up to a factor
1
−
1
/
e
in polynomial time. In the streaming-set model, the sets and their elements are revealed online. The main goal of our work is to design algorithms, with approximation guarantees as close as possible to
1
−
1
/
e
, that use sublinear space
o
(
mn
)
. Our main results are:
Two
(
1
−
1
/
e
−
ε
)
approximation algorithms: One uses
O
(
ε
−
1
)
passes and
Õ
(
ε
−
2
k
)
space whereas the other uses only a single pass but
Õ
(
ε
−
2
m
)
space.
Õ
(
⋅
)
suppresses polylog factors.
We show that any approximation factor better than
(
1
−
(
1
−
1
/
k
)
k
)
≈
1
−
1
/
e
in constant passes requires
Ω
(
m
)
space for constant
k
even if the algorithm is allowed unbounded processing time. We also demonstrate a
single-pass
,
(
1
−
ε
)
approximation algorithm using
Õ
ε
−
2
m
⋅
min
(
k
,
ε
−
1
)
space.
We also study the maximum
k
-vertex coverage problem in the dynamic graph stream model. In this model, the stream consists of edge insertions and deletions of a graph on
N
vertices. The goal is to find
k
vertices that cover the most number of distinct edges.
We show that any constant approximation in constant passes requires
Ω
(
N
)
space for constant
k
whereas
Õ
(
ε
−
2
N
)
space is sufficient for a
(
1
−
ε
)
approximation and arbitrary
k
in a single pass.
For regular graphs, we show that
Õ
(
ε
−
3
k
)
space is sufficient for a
(
1
−
ε
)
approximation in a single pass. We generalize this to a
(
κ
−
ε
)
approximation when the ratio between the minimum and maximum degree is bounded below by
κ
. |
doi_str_mv | 10.1007/s00224-018-9878-x |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2073877805</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2073877805</sourcerecordid><originalsourceid>FETCH-LOGICAL-c359t-ff1b76131ceec7666887ade4dac09a0013ebf46494246adc0ebf84d862b3636f3</originalsourceid><addsrcrecordid>eNp1kE1LAzEQhoMoWKs_wFvAc3SySZMseKnFL6goqOeQ3Z1st3Sbmmyl_nu3ruDJ08zA874DDyHnHC45gL5KAFkmGXDDcqMN2x2QEZdCMJA5HP7sGZNiAsfkJKUlAAgDMCLXN9h1GOlrF9G1zbqm01UdYtMt2kR9iLRbIH1yu6bdtnQWPjG6GulLDMUK21Ny5N0q4dnvHJP3u9u32QObP98_zqZzVopJ3jHveaEVF7xELLVSyhjtKpSVKyF3AFxg4aWSucykclUJ_WlkZVRWCCWUF2NyMfRuYvjYYursMmzjun9pM9DCaG1g0lN8oMoYUoro7SY2rYtfloPdS7KDJNtLsntJdtdnsiGTenZdY_xr_j_0DSrkaew</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2073877805</pqid></control><display><type>article</type><title>Better Streaming Algorithms for the Maximum Coverage Problem</title><source>Business Source Ultimate</source><source>ABI/INFORM Global</source><source>Springer Nature</source><creator>McGregor, Andrew ; Vu, Hoa T.</creator><creatorcontrib>McGregor, Andrew ; Vu, Hoa T.</creatorcontrib><description>We study the classic NP-Hard problem of finding the maximum
k
-set coverage in the data stream model: given a set system of
m
sets that are subsets of a universe
{
1
,
...
,
n
}
, find the
k
sets that cover the most number of distinct elements. The problem can be approximated up to a factor
1
−
1
/
e
in polynomial time. In the streaming-set model, the sets and their elements are revealed online. The main goal of our work is to design algorithms, with approximation guarantees as close as possible to
1
−
1
/
e
, that use sublinear space
o
(
mn
)
. Our main results are:
Two
(
1
−
1
/
e
−
ε
)
approximation algorithms: One uses
O
(
ε
−
1
)
passes and
Õ
(
ε
−
2
k
)
space whereas the other uses only a single pass but
Õ
(
ε
−
2
m
)
space.
Õ
(
⋅
)
suppresses polylog factors.
We show that any approximation factor better than
(
1
−
(
1
−
1
/
k
)
k
)
≈
1
−
1
/
e
in constant passes requires
Ω
(
m
)
space for constant
k
even if the algorithm is allowed unbounded processing time. We also demonstrate a
single-pass
,
(
1
−
ε
)
approximation algorithm using
Õ
ε
−
2
m
⋅
min
(
k
,
ε
−
1
)
space.
We also study the maximum
k
-vertex coverage problem in the dynamic graph stream model. In this model, the stream consists of edge insertions and deletions of a graph on
N
vertices. The goal is to find
k
vertices that cover the most number of distinct edges.
We show that any constant approximation in constant passes requires
Ω
(
N
)
space for constant
k
whereas
Õ
(
ε
−
2
N
)
space is sufficient for a
(
1
−
ε
)
approximation and arbitrary
k
in a single pass.
For regular graphs, we show that
Õ
(
ε
−
3
k
)
space is sufficient for a
(
1
−
ε
)
approximation in a single pass. We generalize this to a
(
κ
−
ε
)
approximation when the ratio between the minimum and maximum degree is bounded below by
κ
.</description><identifier>ISSN: 1432-4350</identifier><identifier>EISSN: 1433-0490</identifier><identifier>DOI: 10.1007/s00224-018-9878-x</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Algorithms ; Approximation ; Computer Science ; Graph theory ; Mathematical analysis ; Set theory ; Special Issue on Database Theory ; Theory of Computation ; Universe</subject><ispartof>Theory of computing systems, 2019-10, Vol.63 (7), p.1595-1619</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2018</rights><rights>Theory of Computing Systems is a copyright of Springer, (2018). All Rights Reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c359t-ff1b76131ceec7666887ade4dac09a0013ebf46494246adc0ebf84d862b3636f3</citedby><cites>FETCH-LOGICAL-c359t-ff1b76131ceec7666887ade4dac09a0013ebf46494246adc0ebf84d862b3636f3</cites><orcidid>0000-0001-8873-0208</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2073877805/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2073877805?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>McGregor, Andrew</creatorcontrib><creatorcontrib>Vu, Hoa T.</creatorcontrib><title>Better Streaming Algorithms for the Maximum Coverage Problem</title><title>Theory of computing systems</title><addtitle>Theory Comput Syst</addtitle><description>We study the classic NP-Hard problem of finding the maximum
k
-set coverage in the data stream model: given a set system of
m
sets that are subsets of a universe
{
1
,
...
,
n
}
, find the
k
sets that cover the most number of distinct elements. The problem can be approximated up to a factor
1
−
1
/
e
in polynomial time. In the streaming-set model, the sets and their elements are revealed online. The main goal of our work is to design algorithms, with approximation guarantees as close as possible to
1
−
1
/
e
, that use sublinear space
o
(
mn
)
. Our main results are:
Two
(
1
−
1
/
e
−
ε
)
approximation algorithms: One uses
O
(
ε
−
1
)
passes and
Õ
(
ε
−
2
k
)
space whereas the other uses only a single pass but
Õ
(
ε
−
2
m
)
space.
Õ
(
⋅
)
suppresses polylog factors.
We show that any approximation factor better than
(
1
−
(
1
−
1
/
k
)
k
)
≈
1
−
1
/
e
in constant passes requires
Ω
(
m
)
space for constant
k
even if the algorithm is allowed unbounded processing time. We also demonstrate a
single-pass
,
(
1
−
ε
)
approximation algorithm using
Õ
ε
−
2
m
⋅
min
(
k
,
ε
−
1
)
space.
We also study the maximum
k
-vertex coverage problem in the dynamic graph stream model. In this model, the stream consists of edge insertions and deletions of a graph on
N
vertices. The goal is to find
k
vertices that cover the most number of distinct edges.
We show that any constant approximation in constant passes requires
Ω
(
N
)
space for constant
k
whereas
Õ
(
ε
−
2
N
)
space is sufficient for a
(
1
−
ε
)
approximation and arbitrary
k
in a single pass.
For regular graphs, we show that
Õ
(
ε
−
3
k
)
space is sufficient for a
(
1
−
ε
)
approximation in a single pass. We generalize this to a
(
κ
−
ε
)
approximation when the ratio between the minimum and maximum degree is bounded below by
κ
.</description><subject>Algorithms</subject><subject>Approximation</subject><subject>Computer Science</subject><subject>Graph theory</subject><subject>Mathematical analysis</subject><subject>Set theory</subject><subject>Special Issue on Database Theory</subject><subject>Theory of Computation</subject><subject>Universe</subject><issn>1432-4350</issn><issn>1433-0490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNp1kE1LAzEQhoMoWKs_wFvAc3SySZMseKnFL6goqOeQ3Z1st3Sbmmyl_nu3ruDJ08zA874DDyHnHC45gL5KAFkmGXDDcqMN2x2QEZdCMJA5HP7sGZNiAsfkJKUlAAgDMCLXN9h1GOlrF9G1zbqm01UdYtMt2kR9iLRbIH1yu6bdtnQWPjG6GulLDMUK21Ny5N0q4dnvHJP3u9u32QObP98_zqZzVopJ3jHveaEVF7xELLVSyhjtKpSVKyF3AFxg4aWSucykclUJ_WlkZVRWCCWUF2NyMfRuYvjYYursMmzjun9pM9DCaG1g0lN8oMoYUoro7SY2rYtfloPdS7KDJNtLsntJdtdnsiGTenZdY_xr_j_0DSrkaew</recordid><startdate>20191001</startdate><enddate>20191001</enddate><creator>McGregor, Andrew</creator><creator>Vu, Hoa T.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0001-8873-0208</orcidid></search><sort><creationdate>20191001</creationdate><title>Better Streaming Algorithms for the Maximum Coverage Problem</title><author>McGregor, Andrew ; Vu, Hoa T.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c359t-ff1b76131ceec7666887ade4dac09a0013ebf46494246adc0ebf84d862b3636f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Algorithms</topic><topic>Approximation</topic><topic>Computer Science</topic><topic>Graph theory</topic><topic>Mathematical analysis</topic><topic>Set theory</topic><topic>Special Issue on Database Theory</topic><topic>Theory of Computation</topic><topic>Universe</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>McGregor, Andrew</creatorcontrib><creatorcontrib>Vu, Hoa T.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><jtitle>Theory of computing systems</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>McGregor, Andrew</au><au>Vu, Hoa T.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Better Streaming Algorithms for the Maximum Coverage Problem</atitle><jtitle>Theory of computing systems</jtitle><stitle>Theory Comput Syst</stitle><date>2019-10-01</date><risdate>2019</risdate><volume>63</volume><issue>7</issue><spage>1595</spage><epage>1619</epage><pages>1595-1619</pages><issn>1432-4350</issn><eissn>1433-0490</eissn><abstract>We study the classic NP-Hard problem of finding the maximum
k
-set coverage in the data stream model: given a set system of
m
sets that are subsets of a universe
{
1
,
...
,
n
}
, find the
k
sets that cover the most number of distinct elements. The problem can be approximated up to a factor
1
−
1
/
e
in polynomial time. In the streaming-set model, the sets and their elements are revealed online. The main goal of our work is to design algorithms, with approximation guarantees as close as possible to
1
−
1
/
e
, that use sublinear space
o
(
mn
)
. Our main results are:
Two
(
1
−
1
/
e
−
ε
)
approximation algorithms: One uses
O
(
ε
−
1
)
passes and
Õ
(
ε
−
2
k
)
space whereas the other uses only a single pass but
Õ
(
ε
−
2
m
)
space.
Õ
(
⋅
)
suppresses polylog factors.
We show that any approximation factor better than
(
1
−
(
1
−
1
/
k
)
k
)
≈
1
−
1
/
e
in constant passes requires
Ω
(
m
)
space for constant
k
even if the algorithm is allowed unbounded processing time. We also demonstrate a
single-pass
,
(
1
−
ε
)
approximation algorithm using
Õ
ε
−
2
m
⋅
min
(
k
,
ε
−
1
)
space.
We also study the maximum
k
-vertex coverage problem in the dynamic graph stream model. In this model, the stream consists of edge insertions and deletions of a graph on
N
vertices. The goal is to find
k
vertices that cover the most number of distinct edges.
We show that any constant approximation in constant passes requires
Ω
(
N
)
space for constant
k
whereas
Õ
(
ε
−
2
N
)
space is sufficient for a
(
1
−
ε
)
approximation and arbitrary
k
in a single pass.
For regular graphs, we show that
Õ
(
ε
−
3
k
)
space is sufficient for a
(
1
−
ε
)
approximation in a single pass. We generalize this to a
(
κ
−
ε
)
approximation when the ratio between the minimum and maximum degree is bounded below by
κ
.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s00224-018-9878-x</doi><tpages>25</tpages><orcidid>https://orcid.org/0000-0001-8873-0208</orcidid><oa>free_for_read</oa></addata></record> |
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issn | 1432-4350 1433-0490 |
language | eng |
recordid | cdi_proquest_journals_2073877805 |
source | Business Source Ultimate; ABI/INFORM Global; Springer Nature |
subjects | Algorithms Approximation Computer Science Graph theory Mathematical analysis Set theory Special Issue on Database Theory Theory of Computation Universe |
title | Better Streaming Algorithms for the Maximum Coverage Problem |
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