KickStarter
Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Vora, Keval [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2017 |
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| Schlagwörter: |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: Operating systems review - New York, NY : ACM, 1970, 51(2017), 2, Seite 237-251 |
|---|---|
| Übergeordnetes Werk: |
volume:51 ; year:2017 ; number:2 ; pages:237-251 |
| Links: |
|---|
| DOI / URN: |
10.1145/3093315.3037748 |
|---|
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OLC1993951652 |
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| 520 | |a Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. | ||
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| 700 | 1 | |a Xu, Guoqing |4 oth | |
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10.1145/3093315.3037748 doi PQ20170901 (DE-627)OLC1993951652 (DE-599)GBVOLC1993951652 (PRQ)acm_primary_30377480 (KEY)0000288720170000051000200237kickstarter DE-627 ger DE-627 rakwb eng 004 DE-600 S418 AVZ rvk Vora, Keval verfasserin aut KickStarter 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. streaming graphs graph processing value dependence Gupta, Rajiv oth Xu, Guoqing oth Enthalten in Operating systems review New York, NY : ACM, 1970 51(2017), 2, Seite 237-251 (DE-627)129615390 (DE-600)243805-7 (DE-576)015113027 0163-5980 nnns volume:51 year:2017 number:2 pages:237-251 http://dx.doi.org/10.1145/3093315.3037748 Volltext http://dl.acm.org/citation.cfm?id=3037748 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2190 GBV_ILN_4317 S418 AR 51 2017 2 237-251 |
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10.1145/3093315.3037748 doi PQ20170901 (DE-627)OLC1993951652 (DE-599)GBVOLC1993951652 (PRQ)acm_primary_30377480 (KEY)0000288720170000051000200237kickstarter DE-627 ger DE-627 rakwb eng 004 DE-600 S418 AVZ rvk Vora, Keval verfasserin aut KickStarter 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. streaming graphs graph processing value dependence Gupta, Rajiv oth Xu, Guoqing oth Enthalten in Operating systems review New York, NY : ACM, 1970 51(2017), 2, Seite 237-251 (DE-627)129615390 (DE-600)243805-7 (DE-576)015113027 0163-5980 nnns volume:51 year:2017 number:2 pages:237-251 http://dx.doi.org/10.1145/3093315.3037748 Volltext http://dl.acm.org/citation.cfm?id=3037748 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2190 GBV_ILN_4317 S418 AR 51 2017 2 237-251 |
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10.1145/3093315.3037748 doi PQ20170901 (DE-627)OLC1993951652 (DE-599)GBVOLC1993951652 (PRQ)acm_primary_30377480 (KEY)0000288720170000051000200237kickstarter DE-627 ger DE-627 rakwb eng 004 DE-600 S418 AVZ rvk Vora, Keval verfasserin aut KickStarter 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. streaming graphs graph processing value dependence Gupta, Rajiv oth Xu, Guoqing oth Enthalten in Operating systems review New York, NY : ACM, 1970 51(2017), 2, Seite 237-251 (DE-627)129615390 (DE-600)243805-7 (DE-576)015113027 0163-5980 nnns volume:51 year:2017 number:2 pages:237-251 http://dx.doi.org/10.1145/3093315.3037748 Volltext http://dl.acm.org/citation.cfm?id=3037748 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2190 GBV_ILN_4317 S418 AR 51 2017 2 237-251 |
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10.1145/3093315.3037748 doi PQ20170901 (DE-627)OLC1993951652 (DE-599)GBVOLC1993951652 (PRQ)acm_primary_30377480 (KEY)0000288720170000051000200237kickstarter DE-627 ger DE-627 rakwb eng 004 DE-600 S418 AVZ rvk Vora, Keval verfasserin aut KickStarter 2017 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. streaming graphs graph processing value dependence Gupta, Rajiv oth Xu, Guoqing oth Enthalten in Operating systems review New York, NY : ACM, 1970 51(2017), 2, Seite 237-251 (DE-627)129615390 (DE-600)243805-7 (DE-576)015113027 0163-5980 nnns volume:51 year:2017 number:2 pages:237-251 http://dx.doi.org/10.1145/3093315.3037748 Volltext http://dl.acm.org/citation.cfm?id=3037748 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_24 GBV_ILN_70 GBV_ILN_2006 GBV_ILN_2190 GBV_ILN_4317 S418 AR 51 2017 2 237-251 |
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Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. |
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Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. |
| abstract_unstemmed |
Continuous processing of a streaming graph maintains an approximate result of the iterative computation on a recent version of the graph. Upon a user query, the accurate result on the current graph can be quickly computed by feeding the approximate results to the iterative computation --- a form of incremental computation that corrects the (small amount of) error in the approximate result. Despite the effectiveness of this approach in processing growing graphs, it is generally not applicable when edge deletions are present --- existing approximations can lead to either incorrect results (e.g., monotonic computations terminate at an incorrect minima/maxima) or poor performance (e.g., with approximations, convergence takes longer than performing the computation from scratch). This paper presents KickStarter, a runtime technique that can trim the approximate values for a subset of vertices impacted by the deleted edges. The trimmed approximation is both safe and profitable, enabling the computation to produce correct results and converge quickly. KickStarter works for a class of monotonic graph algorithms and can be readily incorporated in any existing streaming graph system. Our experiments with four streaming algorithms on five large graphs demonstrate that trimming not only produces correct results but also accelerates these algorithms by 8.5--23.7x. |
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| container_issue |
2 |
| title_short |
KickStarter |
| url |
http://dx.doi.org/10.1145/3093315.3037748 http://dl.acm.org/citation.cfm?id=3037748 |
| remote_bool |
false |
| author2 |
Gupta, Rajiv Xu, Guoqing |
| author2Str |
Gupta, Rajiv Xu, Guoqing |
| ppnlink |
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| doi_str |
10.1145/3093315.3037748 |
| up_date |
2024-07-03T15:55:04.996Z |
| _version_ |
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7.397691 |