کد مقاله کد نشریه سال انتشار مقاله انگلیسی ترجمه فارسی نسخه تمام متن
4951346 1441243 2016 15 صفحه PDF سفارش دهید دانلود کنید
عنوان انگلیسی مقاله ISI
An enhanced Graph Analytics Platform (GAP) providing insight in Big Network Data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
An enhanced Graph Analytics Platform (GAP) providing insight in Big Network Data
چکیده انگلیسی


- The paper presents the enhanced Graph Analytics Platform (GAP).
- GAP allows for data mining promoting a top-down approach for Big Data investigation.
- GAP supports a wide range of key-features, incl. clutter minimization, HR clustering.
- GAP is demonstrated on both a mobile and a social network real-world use case.

Being a widely adapted and acknowledged practice for the representation of inter- and intra-dependent information streams, network graphs are nowadays growing vast in both size and complexity, due to the rapid expansion of sources, types, and amounts of produced data. In this context, the efficient processing of the big amounts of information, also known as Big Data forms a major challenge for both the research community and a wide variety of industrial sectors, involving security, health and financial applications. Serving these emerging needs, the current paper presents a Graph Analytics based Platform (GAP) that implements a top-down approach for the facilitation of Data Mining processes through the incorporation of state-of-the-art techniques, like behavioural clustering, interactive visualizations, multi-objective optimization, etc. The applicability of this platform is validated on 2 istinct real-world use cases, which can be considered as characteristic examples of modern Big Data problems, due to the vast amount of information they deal with. In particular, (i) the root cause analysis of a Denial of Service attack in the network of a mobile operator and (ii) the early detection of an emerging event or a hot topic in social media communities. In order to address the large volume of the data, the proposed application starts with an aggregated overview of the whole network and allows the operator to gradually focus on smaller sets of data, using different levels of abstraction. The proposed platform offers differentiation between different user behaviors that enable the analyst to obtain insight on the network's operation and to extract the meaningful information in an effortless manner. Dynamic hypothesis formulation techniques exploited by graph traversing and pattern mining, enable the analyst to set concrete network-related hypotheses, and validate or reject them accordingly.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Innovation in Digital Ecosystems - Volume 3, Issue 2, December 2016, Pages 83-97
نویسندگان
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