P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network
Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P netw...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Farzanyar, Zahra [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013transfer abstract |
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| Schlagwörter: |
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| Umfang: |
6 |
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| Übergeordnetes Werk: |
Enthalten in: Galectin-3 in Atrial Fibrillation: A Novel Marker of Atrial Remodeling or Just Bystander? - Kornej, Jelena ELSEVIER, 2015, devoted to the rapid publication of short contributions to information processing, Amsterdam [u.a.] |
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| Übergeordnetes Werk: |
volume:113 ; year:2013 ; number:19 ; pages:793-798 ; extent:6 |
| Links: |
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| DOI / URN: |
10.1016/j.ipl.2013.07.016 |
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ELV039097196 |
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| 520 | |a Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. | ||
| 520 | |a Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. | ||
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10.1016/j.ipl.2013.07.016 doi GBVA2013021000010.pica (DE-627)ELV039097196 (ELSEVIER)S0020-0190(13)00205-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 510 VZ 31.80 bkl Farzanyar, Zahra verfasserin aut P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network 2013transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. P2P distributed systems Elsevier Data stream Elsevier P2P data mining Elsevier On-line algorithms Elsevier Kangavari, Mohammadreza oth Cercone, Nick oth Enthalten in Elsevier Kornej, Jelena ELSEVIER Galectin-3 in Atrial Fibrillation: A Novel Marker of Atrial Remodeling or Just Bystander? 2015 devoted to the rapid publication of short contributions to information processing Amsterdam [u.a.] (DE-627)ELV023909307 volume:113 year:2013 number:19 pages:793-798 extent:6 https://doi.org/10.1016/j.ipl.2013.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_62 GBV_ILN_70 GBV_ILN_72 GBV_ILN_77 GBV_ILN_110 GBV_ILN_120 GBV_ILN_176 31.80 Angewandte Mathematik VZ AR 113 2013 19 793-798 6 045F 004 |
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10.1016/j.ipl.2013.07.016 doi GBVA2013021000010.pica (DE-627)ELV039097196 (ELSEVIER)S0020-0190(13)00205-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 510 VZ 31.80 bkl Farzanyar, Zahra verfasserin aut P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network 2013transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. P2P distributed systems Elsevier Data stream Elsevier P2P data mining Elsevier On-line algorithms Elsevier Kangavari, Mohammadreza oth Cercone, Nick oth Enthalten in Elsevier Kornej, Jelena ELSEVIER Galectin-3 in Atrial Fibrillation: A Novel Marker of Atrial Remodeling or Just Bystander? 2015 devoted to the rapid publication of short contributions to information processing Amsterdam [u.a.] (DE-627)ELV023909307 volume:113 year:2013 number:19 pages:793-798 extent:6 https://doi.org/10.1016/j.ipl.2013.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_62 GBV_ILN_70 GBV_ILN_72 GBV_ILN_77 GBV_ILN_110 GBV_ILN_120 GBV_ILN_176 31.80 Angewandte Mathematik VZ AR 113 2013 19 793-798 6 045F 004 |
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10.1016/j.ipl.2013.07.016 doi GBVA2013021000010.pica (DE-627)ELV039097196 (ELSEVIER)S0020-0190(13)00205-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 510 VZ 31.80 bkl Farzanyar, Zahra verfasserin aut P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network 2013transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. P2P distributed systems Elsevier Data stream Elsevier P2P data mining Elsevier On-line algorithms Elsevier Kangavari, Mohammadreza oth Cercone, Nick oth Enthalten in Elsevier Kornej, Jelena ELSEVIER Galectin-3 in Atrial Fibrillation: A Novel Marker of Atrial Remodeling or Just Bystander? 2015 devoted to the rapid publication of short contributions to information processing Amsterdam [u.a.] (DE-627)ELV023909307 volume:113 year:2013 number:19 pages:793-798 extent:6 https://doi.org/10.1016/j.ipl.2013.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_62 GBV_ILN_70 GBV_ILN_72 GBV_ILN_77 GBV_ILN_110 GBV_ILN_120 GBV_ILN_176 31.80 Angewandte Mathematik VZ AR 113 2013 19 793-798 6 045F 004 |
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10.1016/j.ipl.2013.07.016 doi GBVA2013021000010.pica (DE-627)ELV039097196 (ELSEVIER)S0020-0190(13)00205-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 510 VZ 31.80 bkl Farzanyar, Zahra verfasserin aut P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network 2013transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. P2P distributed systems Elsevier Data stream Elsevier P2P data mining Elsevier On-line algorithms Elsevier Kangavari, Mohammadreza oth Cercone, Nick oth Enthalten in Elsevier Kornej, Jelena ELSEVIER Galectin-3 in Atrial Fibrillation: A Novel Marker of Atrial Remodeling or Just Bystander? 2015 devoted to the rapid publication of short contributions to information processing Amsterdam [u.a.] (DE-627)ELV023909307 volume:113 year:2013 number:19 pages:793-798 extent:6 https://doi.org/10.1016/j.ipl.2013.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_62 GBV_ILN_70 GBV_ILN_72 GBV_ILN_77 GBV_ILN_110 GBV_ILN_120 GBV_ILN_176 31.80 Angewandte Mathematik VZ AR 113 2013 19 793-798 6 045F 004 |
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10.1016/j.ipl.2013.07.016 doi GBVA2013021000010.pica (DE-627)ELV039097196 (ELSEVIER)S0020-0190(13)00205-6 DE-627 ger DE-627 rakwb eng 004 004 DE-600 610 VZ 510 VZ 31.80 bkl Farzanyar, Zahra verfasserin aut P2P-FISM: Mining (recently) frequent item sets from distributed data streams over P2P network 2013transfer abstract 6 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. P2P distributed systems Elsevier Data stream Elsevier P2P data mining Elsevier On-line algorithms Elsevier Kangavari, Mohammadreza oth Cercone, Nick oth Enthalten in Elsevier Kornej, Jelena ELSEVIER Galectin-3 in Atrial Fibrillation: A Novel Marker of Atrial Remodeling or Just Bystander? 2015 devoted to the rapid publication of short contributions to information processing Amsterdam [u.a.] (DE-627)ELV023909307 volume:113 year:2013 number:19 pages:793-798 extent:6 https://doi.org/10.1016/j.ipl.2013.07.016 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OPC-MAT GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_30 GBV_ILN_62 GBV_ILN_70 GBV_ILN_72 GBV_ILN_77 GBV_ILN_110 GBV_ILN_120 GBV_ILN_176 31.80 Angewandte Mathematik VZ AR 113 2013 19 793-798 6 045F 004 |
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Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. |
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Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. |
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Data intensive large-scale distributed systems like peer-to-peer (P2P) networks are finding large number of applications for social networking, file sharing networks, etc. Global data mining in such P2P environments may be very costly due to the high scale and the asynchronous nature of the P2P networks. The cost further increases in the distributed data stream scenario where peers receive continuous sequence of transactions rapidly. In this paper, we develop an efficient local algorithm, P2P-FISM, for discovering of the network-wide recent frequent itemsets. The algorithm works in a completely asynchronous manner, imposes low communication overhead, a necessity for scalability, transparently tolerates network topology changes, and quickly adapts to changes in the data stream. The paper demonstrates experimental results to corroborate the theoretical claims. |
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