Novel method for optimizing performance in resource constrained distributed data streams
Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bhalla, Rashi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Applied intelligence - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991, 52(2022), 11 vom: 16. Feb., Seite 12924-12942 |
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| Übergeordnetes Werk: |
volume:52 ; year:2022 ; number:11 ; day:16 ; month:02 ; pages:12924-12942 |
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| DOI / URN: |
10.1007/s10489-021-03019-5 |
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SPR048121614 |
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| 520 | |a Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. | ||
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| 700 | 1 | |a Mirza, Farhaan |4 aut | |
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10.1007/s10489-021-03019-5 doi (DE-627)SPR048121614 (SPR)s10489-021-03019-5-e DE-627 ger DE-627 rakwb eng Bhalla, Rashi verfasserin aut Novel method for optimizing performance in resource constrained distributed data streams 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. Big data (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Distributed data stream mining (dpeaa)DE-He213 Heterogeneous distributed data (dpeaa)DE-He213 Pears, Russel aut Naeem, M. Asif aut Mirza, Farhaan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 11 vom: 16. Feb., Seite 12924-12942 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:11 day:16 month:02 pages:12924-12942 https://dx.doi.org/10.1007/s10489-021-03019-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 11 16 02 12924-12942 |
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10.1007/s10489-021-03019-5 doi (DE-627)SPR048121614 (SPR)s10489-021-03019-5-e DE-627 ger DE-627 rakwb eng Bhalla, Rashi verfasserin aut Novel method for optimizing performance in resource constrained distributed data streams 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. Big data (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Distributed data stream mining (dpeaa)DE-He213 Heterogeneous distributed data (dpeaa)DE-He213 Pears, Russel aut Naeem, M. Asif aut Mirza, Farhaan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 11 vom: 16. Feb., Seite 12924-12942 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:11 day:16 month:02 pages:12924-12942 https://dx.doi.org/10.1007/s10489-021-03019-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 11 16 02 12924-12942 |
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10.1007/s10489-021-03019-5 doi (DE-627)SPR048121614 (SPR)s10489-021-03019-5-e DE-627 ger DE-627 rakwb eng Bhalla, Rashi verfasserin aut Novel method for optimizing performance in resource constrained distributed data streams 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. Big data (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Distributed data stream mining (dpeaa)DE-He213 Heterogeneous distributed data (dpeaa)DE-He213 Pears, Russel aut Naeem, M. Asif aut Mirza, Farhaan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 11 vom: 16. Feb., Seite 12924-12942 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:11 day:16 month:02 pages:12924-12942 https://dx.doi.org/10.1007/s10489-021-03019-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 11 16 02 12924-12942 |
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10.1007/s10489-021-03019-5 doi (DE-627)SPR048121614 (SPR)s10489-021-03019-5-e DE-627 ger DE-627 rakwb eng Bhalla, Rashi verfasserin aut Novel method for optimizing performance in resource constrained distributed data streams 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. Big data (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Distributed data stream mining (dpeaa)DE-He213 Heterogeneous distributed data (dpeaa)DE-He213 Pears, Russel aut Naeem, M. Asif aut Mirza, Farhaan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 11 vom: 16. Feb., Seite 12924-12942 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:11 day:16 month:02 pages:12924-12942 https://dx.doi.org/10.1007/s10489-021-03019-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 11 16 02 12924-12942 |
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10.1007/s10489-021-03019-5 doi (DE-627)SPR048121614 (SPR)s10489-021-03019-5-e DE-627 ger DE-627 rakwb eng Bhalla, Rashi verfasserin aut Novel method for optimizing performance in resource constrained distributed data streams 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. Big data (dpeaa)DE-He213 Bayesian inference (dpeaa)DE-He213 Distributed data stream mining (dpeaa)DE-He213 Heterogeneous distributed data (dpeaa)DE-He213 Pears, Russel aut Naeem, M. Asif aut Mirza, Farhaan aut Enthalten in Applied intelligence Dordrecht [u.a.] : Springer Science + Business Media B.V, 1991 52(2022), 11 vom: 16. Feb., Seite 12924-12942 (DE-627)271180919 (DE-600)1479519-X 1573-7497 nnns volume:52 year:2022 number:11 day:16 month:02 pages:12924-12942 https://dx.doi.org/10.1007/s10489-021-03019-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 52 2022 11 16 02 12924-12942 |
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Novel method for optimizing performance in resource constrained distributed data streams |
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Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstractGer |
Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract The Big Data Era has presented many opportunities for using data mining techniques to discover knowledge patterns across large and diverse collections of data where the volume of data is growing at an exponential rate. Recent approaches to Distributed Data Mining (DDM) have focused on addressing the heterogeneous nature of data sources. However, such approaches do not prioritize the reduction of data communication costs which could be prohibitive in large scale sensor networks where bandwidth is a limited resource. In fact, higher communication and computational costs are the two most prominent problems that have been encountered in heterogeneous distributed environments. Moreover, an effort to decrease the communications load in the distributed environment has an adverse influence on the classification accuracy. Therefore, the research challenge lies in maintaining a balance between transmission cost, computational cost, and accuracy. This paper proposes an algorithm Performance Optimizer in Distributed Stream Mining (PODSM) based on Bayesian Inference to reduce the communication volume and resource time in a heterogeneous distributed data mining environment while retaining prediction accuracy. The approach used in this work exploits the past data for calculating statistics and these statistics are then utilized for the new data. In other words, it imparts the ability to learn from experiences. As a result, our experimental evaluation reveals that a significant reduction in the communication load and an improvement in classification response time can be achieved across a diverse range of dataset types. Reduction of 34.66% was obtained with regard to communication overhead for one of the datasets with huge savings of nearly 27% in resource time. Importantly, instead of showing a negative effect on accuracy, this dataset observes an increment of 0.44% in accuracy. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022 |
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Novel method for optimizing performance in resource constrained distributed data streams |
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https://dx.doi.org/10.1007/s10489-021-03019-5 |
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Pears, Russel Naeem, M. Asif Mirza, Farhaan |
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10.1007/s10489-021-03019-5 |
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| score |
7.401636 |