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AutoDiagn: An Automated Real-time Diagnosis Framework for Big Data Systems

Big data processing systems, such as Hadoop and Spark, usually work on large-scale, highly-concurrent, and multi-tenant environments that can easily cause hardware and software malfunctions or failures, thereby leading to performance degradation. …

Use of Social Media Data in Disaster Management: A Survey

Abstract: Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of …

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a …

Active Hazard Observation via Human in the Loop Social Media Analytics System

We demonstrate AHOM, a system that can Actively Observe Hazards via Monitoring Social Media Streams. AHOM proposes an active way to include the human in the loop of hazard information ac-quisition for social media. Different from state of the art, it …

Blockchain based Task Offloading in Drone-aided Mobile Edge Computing

Abstract—An increasing number of cloud providers now offer Mobile Edge Computing (MEC) services for their customers to support task offloading. This is undertaken to reduce latency associated with forwarding data from IoT devices owned by customers …

Efficient Graph Query Processing over Geo-Distributed Datacenters

Graph queries have emerged as one of the fundamental techniques to support modern search services, such as PageRank web search, social networking search and knowledge graph search. As such graphs are maintained globally and very huge (e.g., billions …

Orchestrating the Development Lifecycle of Machine Learning Based IoT Applications: A Survey

Machine Learning (ML) and Internet of Things (IoT) are complementary advances: ML techniques unlock complete potentials of IoT with intelligence, and IoT applications increasingly feed data collected by sensors into ML models, thereby employing …

Performance-aware Speculative Resource Oversubscription for Large-scale Clusters.

It is a long-standing challenge to achieve a high degree of resource utilization in cluster scheduling. Resource oversubscription has become a common practice in improving resource utilization and cost reduction. However, current centralized …

R-print: A System Residuals-based Fingerprinting for Attack Detection in Industrial Cyber-physical Systems

Industrial cyber-physical systems (ICPS) are widely used to facilitate accurately remote control in industrial application fields using cyberspace technologies. However, it is easily suffered from internal vulnerabilities and other external threats …

Running Industrial Workflow Applications in a Software-defined Multi-Cloud Environment using Green Energy Aware Scheduling Algorithm

Abstract—Industry 4.0 have automated the entire manufacturing sector (including technologies and processes) by adopting Internet of Things and Cloud computing. To handle the workflows from Industrial Cyber-Physical systems, more and more data centers …