scholarly journals JSForce: A Forced Execution Engine for Malicious JavaScript Detection

2018 ◽  
pp. 704-720 ◽  
Author(s):  
Xunchao Hu ◽  
Yao Cheng ◽  
Yue Duan ◽  
Andrew Henderson ◽  
Heng Yin
Keyword(s):  
Execution Engine

scholarly journals J-CO: A Platform-Independent Framework for Managing Geo-Referenced JSON Data Sets

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 621
Author(s):  
Giuseppe Psaila ◽  
Paolo Fosci
Keyword(s):  
Query Language ◽  
Open Data ◽  
Internet Technology ◽  
Data Sets ◽  
Specific Storage ◽  
Current State ◽  
Execution Engine ◽  
Share Data ◽  
Cloud Servers ◽  
Computational Resources

Internet technology and mobile technology have enabled producing and diffusing massive data sets concerning almost every aspect of day-by-day life. Remarkable examples are social media and apps for volunteered information production, as well as Open Data portals on which public administrations publish authoritative and (often) geo-referenced data sets. In this context, JSON has become the most popular standard for representing and exchanging possibly geo-referenced data sets over the Internet.Analysts, wishing to manage, integrate and cross-analyze such data sets, need a framework that allows them to access possibly remote storage systems for JSON data sets, to retrieve and query data sets by means of a unique query language (independent of the specific storage technology), by exploiting possibly-remote computational resources (such as cloud servers), comfortably working on their PC in their office, more or less unaware of real location of resources. In this paper, we present the current state of the J-CO Framework, a platform-independent and analyst-oriented software framework to manipulate and cross-analyze possibly geo-tagged JSON data sets. The paper presents the general approach behind the J-CO Framework, by illustrating the query language by means of a simple, yet non-trivial, example of geographical cross-analysis. The paper also presents the novel features introduced by the re-engineered version of the execution engine and the most recent components, i.e., the storage service for large single JSON documents and the user interface that allows analysts to comfortably share data sets and computational resources with other analysts possibly working in different places of the Earth globe. Finally, the paper reports the results of an experimental campaign, which show that the execution engine actually performs in a more than satisfactory way, proving that our framework can be actually used by analysts to process JSON data sets.

Hardware Implementation on FPGA for Task-Level Parallel Dataflow Execution Engine

2016 ◽  
Vol 27 (8) ◽  
pp. 2303-2315 ◽  
Author(s):  
Chao Wang ◽  
Junneng Zhang ◽  
Xi Li ◽  
Aili Wang ◽  
Xuehai Zhou
Keyword(s):  
Hardware Implementation ◽  
Execution Engine ◽  
Dataflow Execution ◽  
Task Level

Scalable mining of maximal quasi-cliques

2020 ◽  
Vol 14 (4) ◽  
pp. 573-585
Author(s):  
Guimu Guo ◽  
Da Yan ◽  
M. Tamer Özsu ◽  
Zhe Jiang ◽  
Jalal Khalil
Keyword(s):  
Graph Mining ◽  
State Of The Art ◽  
Minimum Degree ◽  
The Other ◽  
Dense Subgraph ◽  
Load Imbalance ◽  
Subgraph Mining ◽  
Execution Engine ◽  
Clique Algorithm ◽  
Dense Subgraph Mining

Given a user-specified minimum degree threshold γ , a γ -quasiclique is a subgraph g = (V g , E g ) where each vertex ν ∈ V g connects to at least γ fraction of the other vertices (i.e., ⌈ γ · (| V g |- 1)⌉ vertices) in g. Quasi-clique is one of the most natural definitions for dense structures useful in finding communities in social networks and discovering significant biomolecule structures and pathways. However, mining maximal quasi-cliques is notoriously expensive. In this paper, we design parallel algorithms for mining maximal quasi-cliques on G-thinker, a distributed graph mining framework that decomposes mining into compute-intensive tasks to fully utilize CPU cores. We found that directly using G-thinker results in the straggler problem due to (i) the drastic load imbalance among different tasks and (ii) the difficulty of predicting the task running time. We address these challenges by redesigning G-thinker's execution engine to prioritize long-running tasks for execution, and by utilizing a novel timeout strategy to effectively decompose long-running tasks to improve load balancing. While this system redesign applies to many other expensive dense subgraph mining problems, this paper verifies the idea by adapting the state-of-the-art quasi-clique algorithm, Quick, to our redesigned G-thinker. Extensive experiments verify that our new solution scales well with the number of CPU cores, achieving 201× runtime speedup when mining a graph with 3.77M vertices and 16.5M edges in a 16-node cluster.

scholarly journals E3: an Elastic Execution Engine for Scalable Data Processing

2012 ◽  
Vol 20 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Gang Chen ◽  
Ke Chen ◽  
Dawei Jiang ◽  
Beng Chin Ooi ◽  
Lei Shi ◽  
...  
Keyword(s):  
Data Processing ◽  
Execution Engine

PEEP: A Parallel Execution Engine for Permissioned Blockchain Systems

2021 ◽  
pp. 341-357
Author(s):  
Zhihao Chen ◽  
Xiaodong Qi ◽  
Xiaofan Du ◽  
Zhao Zhang ◽  
Cheqing Jin
Keyword(s):  
Parallel Execution ◽  
Execution Engine
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