Smart Cloud Caching for Data Intensive Applications
As Cloud computing is gaining popularity among small and medium enterprises, Cloud storage solutions such as Amazon S3 are increasingly utilized for storing, maintaining, and serving application data. Despite the typical high-speed internet connections between applications and Cloud storage, there is still a huge performance gap compared to accessing data from direct-attached memory or even locally attached disks. SMACC is a novel Cloud caching service developed at CUT that can run on application compute nodes (e.g., on Amazon EC2) and cache frequently-used data residing on Amazon S3 into local memory and locally-attached disks (e.g., Amazon EBS) using new smart policies. SMACC also provides an HDFS-compatible API interface, which can be used by big data platforms such as Spark and Hadoop for processing data residing on Amazon S3, while caching data blocks on the various compute nodes for increased performance.
Real-time Aggression Detection on Social Media
The rise of online aggression on social media is evolving into a major point of concern. Several machine and deep learning approaches have been proposed recently for detecting various types of aggressive behavior. However, social media are fast paced, generating an increasing amount of content, while aggressive behavior evolves over time. We introduce the first practical, real-time framework for detecting aggression on Twitter by embracing the streaming machine-learning paradigm. The framework is designed to be adaptable (its ML classifiers are trained incrementally as they receive new annotated examples), scalable (it can process the entire Twitter Firehose with three machines), and generalizable (it can detect other abusive behaviors such as sarcasm, racism, and sexism in real time). This project is done in collaboration with Dr Nicolas Kourtellis from Telefonica Research, Spain and Dr Despoina Chatzakou from Centre for Research and Technology Hellas, Greece.
Data-Driven Tourist Destination Marketing
This project employs a machine-learning approach to tourist destination marketing campaigns through the analysis of tourists’ reviews from TripAdvisor to identify significant patterns in the data. The proposed methodology combines topic modelling using Structured Topic Analysis with sentiment polarity, information on culture, and purchasing power of tourists for the development Decision Trees (DTs) at different level of granularity. The goal is to identify patterns in tourists’ accommodation experiences and potential reasons for their dissatisfaction and satisfaction, which in turn can improve destination marketing and optimize a destination’s profitability. This project is done in collaboration with Dr Andreas Gregoriades from Cyprus University of Technology and Dr Maria Pampaka from The University of Manchester, UK.
Maritime Cognitive Decision Support System
The primary general objective of the MARI-Sense project is the integration and adaptation of existing expertise and the development of novel knowledge and skills to develop the MARI-Sense Cognitive Decision Support System for Maritime Activities Planning, Emergency Response and Planning, and Maritime Spatial Planning. The secondary general objective is the development and implementation of strategies for smart, sustainable, and inclusive growth with beneficial impact to the society, technology, and economy powered by the diverse capabilities of members of the quadruple helix and general public. The project is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation (RIF) with a total budget of 1M Euros.
Sea Traffic Management in the Eastern Mediterranean
The general objective of the STEAM (Sea Traffic Management in the Eastern Mediterranean) project is the efficient management of sea traffic in the Eastern Mediterranean sea, while at the same time ensuring safety and environmental sustainability. More specifically, to develop the Port of Limassol to become (i) a world-class transshipment and information hub adopting modern digital technologies brought to the maritime sector, and (ii) a driver for short sea shipping in the Eastern Mediterranean through enhanced services based on standardized ship and port connectivity. The project is co-funded by the European Regional Development Fund and the Republic of Cyprus through the Research and Innovation Foundation (RIF) with a total budget of 1M Euros.
Scaling Transactional Databases with Strong Guarantees
Database replication is a common mechanism used for scaling performance and improving availability of transactional databases but past approaches have suffered from various issues including limited scalability, performance versus consistency tradeoffs, and requirements for database or application modifications. Hihooi is a replication-based middleware solution that employs a novel shared-nothing architecture, a fast replication algorithm, and a light-weight transaction scheduling algorithm in order to provide both workload scalability and strong consistency. This project is done in collaboration with Dr. Michael Sirivianos from Cyprus University of Technology.
Distributed Multi-tier Storage for Cluster Computing
Improvements in memory, storage devices, and network technologies are constantly exploited by distributed systems in order to meet the increasing data storage and I/O demands of modern large-scale data analytics. We present a novel distributed file system that is aware of storage media (e.g., memory, SSDs, HDDs, NAS) with different capacities and performance characteristics. The system offers a spectrum of usage patterns ranging from fully automating data management to providing explicit control by exposing the storage tiers to users. This project is funded by the Cyprus University of Technology Starting Grant.