Incremental Maintenance of Online Summaries Over Multiple Streams

Altiparmak, Fatih; Tuncel, Ertem; Ferhatosmanoğlu, Hakan

doi:10.1109/tkde.2007.190693

Cited by 22 publications

(14 citation statements)

References 23 publications

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“…Its counterpart, HASTE [186], handles time critical events by satisfying time constraints while seeking to maximize an application specific benefit function. In other works utilize discretization approaches, such as predictive quantization [8] to represent the original streaming data in a sliding window timeframe as accurately as possible. In a related effort, we proposed a method to "age" and shed accuracy for older, quantized elements within the sliding window by removing bits dynamically for their representation [7].…”

Section: Scientific Data Processingmentioning

confidence: 99%

Auspice: Automatic Service Planning in Cloud/Grid Environments

Chiu

Agrawal

2011

Grids, Clouds and Virtualization

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Scientific advancements have ushered in staggering amounts of available data and processes which are now scattered across various locations in the Web, Grid, and more recently, the Cloud. These processes and data sets are often semantically loosely-coupled and must be composed together piecemeal to generate scientific workflows. Understanding how to design, manage, and execute such data-intensive workflows has become increasingly esoteric, confined to a few scientific experts in the field. Despite the development of scientific workflow management systems, which have simplified workflow planning to some extent, a means to reduce the complexity of user interaction without forfeiting some robustness has been elusive. This violates the essence of scientific progress, where information should be accessible to anyone.A high-level querying interface tantamount to common search engines that can not only return a relevant set of scientific workflows, but also facilitate their execution, may be highly beneficial to users.The development of such a system that can abstract the complex task of scientific workflow planning and execution from the user is reported herein. Our system, Auspice: AUtomatic Service Planning In Cloud/Grid Environments, consists of the following key contributions. Initially, a two-level metadata management framework is introduced. In the top-level, Auspice captures semantic dependencies among available, shared processes and data sets with an ontology. Our system furthermore indexes these shared resources for facilitating fast planning times. This metadata ii To my family, friends, and mentors. iv ACKNOWLEDGMENTS This work is far from complete without proper acknowledgment of those who supported me over the years. My family. When my parents made the decision to move to Ohio from Taiwan over twenty years ago, it was not the ideal situation for either of them. My mother spent tireless hours teaching me English and we struggled through heaps of schoolwork together. My father kept the family afloat through his ongoing work overseas. He also brought me up with great values in life, including humility and patience. Without their sacrifice and guidance, my education would have been impossible. I also thank my sister, Jenn, who has always been my partner in crime in childhood and now.My wife, Michelle, for her infinite patience and unwavering love and understanding during even the most trying times. I am thankful for the many drop-offs and pick-ups to/from Dreese Labs she has had to perform on early mornings and late evenings. I respect her deeply, and she is a great inspiration to me.

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Section: Scientific Data Processingmentioning

confidence: 99%

Auspice: Automatic Service Planning in Cloud/Grid Environments

Chiu

Agrawal

2011

Grids, Clouds and Virtualization

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“…Then the relationships between the new data added and the data structure stored are analyzed in order to require new knowledge. Therefore, in real-life applications, information systems may be very large [27][28][29][30] and vary with time. In FRS, the generation of fuzzy relations among samples is often very costly or even intractable.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy rough set approach for incremental feature selection on hybrid information systems

Zeng

Liu

et al. 2015

Fuzzy Sets and Systems

185

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“…The update of approximations is a necessary step for knowledge representation and reduction based on RST in dynamic information systems. An incremental algorithm is considered to be an effective approach in a dynamic information processing by taking full advantage of the existing information and thus avoiding unnecessary recomputation . There has been much research about employing incremental approaches to update approximations in RST.…”

Section: Introductionmentioning

confidence: 99%

“…An incremental algorithm is considered to be an effective approach in a dynamic information processing by taking full advantage of the existing information and thus avoiding unnecessary recomputation. 24 There has been much research about employing incremental approaches to update approximations in RST. According to the variation of information systems, the incremental update approximations methods can be divided into three kinds:…”

Section: Introductionmentioning

confidence: 99%

An Incremental Learning Approach for Updating Approximations in Rough Set Model over Dual Universes

Chen

et al. 2015

Int. J. Intell. Syst.

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The rough set model over dual universes (RSMDU) as a generalized model of classical rough set theory (RST) on the two universes has been well studied with the objective to establishment of model and discussion of its corresponding properties. Approximations of a concept in RSMDU, which may further be applied to knowledge discovery or related work, need to be updated effectively under a dynamic environment. Despite recent advances in using the incremental method to speed up updating approximations of RST, there has been little effort toward incorporating the incremental method into computing approximations under RSMDU. This paper proposes an incremental learning approach for updating approximations in RSMDU when the objects of two universes vary with time. An illustration is employed to show the proposed method. Extensive experimental results on various real and synthetic data sets verify the effectiveness of the proposed incremental updating method while comparing with the nonincremental method.

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Incremental Maintenance of Online Summaries Over Multiple Streams

Cited by 22 publications

References 23 publications

Auspice: Automatic Service Planning in Cloud/Grid Environments

Auspice: Automatic Service Planning in Cloud/Grid Environments

A fuzzy rough set approach for incremental feature selection on hybrid information systems

An Incremental Learning Approach for Updating Approximations in Rough Set Model over Dual Universes

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