Co-location patterns in spatial dataset are the interesting collection of dissimilar objects which are located in proximity. We keep similar objects in an entity set and maintain that no two objects in a co-location p...
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Co-location patterns in spatial dataset are the interesting collection of dissimilar objects which are located in proximity. We keep similar objects in an entity set and maintain that no two objects in a co-location pattern belong to an entity set. Location proximity is based on Euclidean distance measure. However, algorithms for mining patterns in transactional datasets are not directly applicable to spatial datasets for mining co-location patterns. Conventional methods are not applicable to distributed tempo-ral data and many applications generating spatial dataset are inherently distributive in nature. In this paper, a map-reduce based approach is proposed to find all co-location patterns from a spatial dataset distributed over nodes. This approach is modularized one and consists of four algorithms. With the first three algorithms in the first approach and by proposing an algorithm for dynamic datasets, this paper contains another approach for the co-location patterns set, that also updates in an incremental manner (not from scratch) whenever certain changes occur in the dataset. Experimental results on larger datasets are also presented. (c) 2018 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
map-reduce, the cornerstone computational framework for cloud computing applications, has star appeal to draw students to the study of parallelism. Participants will carry out hands-on exercises designed for students ...
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ISBN:
(纸本)9781450318686
map-reduce, the cornerstone computational framework for cloud computing applications, has star appeal to draw students to the study of parallelism. Participants will carry out hands-on exercises designed for students at CS1/intermediate/advanced levels that introduce data-intensive scalable computing concepts, using Webmapreduce (WMR), a simplified open-source interface to the widely used Hadoop map-reduce programming environment. These hands-on exercises enable students to perform data-intensive scalable computations carried out on the most widely deployed map-reduce framework, used by Facebook, Microsoft, Yahoo, and other companies. WMR supports programming in a choice of languages (including Java, Python, C++, C#, Scheme); participants will be able to try exercises with languages of their choice. Workshop includes brief introduction to direct Hadoop programming, and information about access to cluster resources supporting WMR. Workshop materials will reside on ***, along with WMR software. Intended audience: CS instructors. Laptop required (Windows, Mac, or Linux).
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