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Application of graph databases in the communication and information asset management in power grid
Published in Lin Liu, Automotive, Mechanical and Electrical Engineering, 2017
Xuming Lv, Shanqi Zheng, Zhao Li, Siyan Liu, Yue Wang
Graph processing platforms have played more and more important roles in large online system with deep links between records. Pregel is the system built by Google to power PageRank, which is a fundamental algorithm for web searching engine. It is also the inspiration for Apache Giraph, which Facebook uses to analyze their social graph. However, Pregel is owned by Google and unavailable to other developers. Apache Giraph only releases the first version in 2015 and releases no update.
Malicious accounts detection from online social networks: a systematic review of literature
Published in International Journal of General Systems, 2021
Imen Ben Sassi, Sadok Ben Yahia
One of the key challenges of using OSNs data is the consideration of data streams and the design of an online real-time solution. In malicious accounts detection, we focused most of the studies on the batch processing mode by validating their models using a test set. Only Miller et al. handled the streaming nature of online posts, particularly tweets, while proposing their anomaly detection system aiming to cluster streaming data (Miller et al. 2014). In the big data domain, we refer to this challenge as the velocity problem. For example, bots spreading fake news in OSNs are usually short-lived accounts. After the 2016 U.S. election, most news accounts no longer exist. We can see this behavior as a strategy to evade detection from the OSN immune systems. The real-time detection of misbehavior is very difficult and identifying the malicious entities engaged to spread the online content in terms of data storage and computation. Thus, a distributed solution needs to be implemented for rapid and efficient analysis. Two frameworks are available to address the problem of mining large social network datasets using graph and machine learning methods for malicious behavior detection. Apache Giraph14 is a graph processing framework designed to address the scalability of large-scale graph-based algorithms. Apache Mahout15 is a framework that provides distributed implementations of machine learning algorithms.
Programming models and systems for Big Data analysis
Published in International Journal of Parallel, Emergent and Distributed Systems, 2019
Loris Belcastro, Fabrizio Marozzo, Domenico Talia
Apache Giraph12 is an iterative graph processing system built for developing high scalable applications. In 2012, when the first version of Giraph was released, it was considered the open source implementation of Pregel [20], a graph processing architecture developed at Google. Compared to Pregel, Giraph introduces several enhancements, such as master computation, sharded aggregators, edge-oriented input, out-of-core computation.