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Introducing distributed dynamic data‐intensive (D3) science: Understanding applications and infrastructure
Author(s) -
Jha Shantenu,
Katz Daniel S.,
Luckow Andre,
Chue Hong  Neil,
Rana Omer,
Simmhan Yogesh
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4032
Subject(s) - computer science , dynamic data , distributed computing , data science , software , e science , computation , feature (linguistics) , data mining , database , philosophy , geometry , mathematics , algorithm , programming language , grid , linguistics
Summary A common feature across many science and engineering applications is the amount and diversity of data and computation that must be integrated to yield insights. Datasets are growing larger and becoming distributed; their location, availability, and properties are often time‐dependent. Collectively, these characteristics give rise to dynamic distributed data‐intensive applications. While “static” data applications have received significant attention, the characteristics, requirements, and software systems for the analysis of large volumes of dynamic, distributed data, and data‐intensive applications have received relatively less attention. This paper surveys several representative dynamic distributed data‐intensive application scenarios, provides a common conceptual framework to understand them, and examines the infrastructure used in support of applications.

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