This page has only limited features, please log in for full access.
This paper presents a machine learning approach involving tourists’ electronic word of mouth (eWOM) to support destination marketing campaigns. This approach enhances optimisation of a critical aspect of marketing campaigns, that is, the communication of the right content to the right consumers. The proposed method further considers aggregate cultural and economic-related information of the tourists’ country of origin with topic modelling and Decision Tree (DT) models. Each DT addresses different dimensions of culture and purchasing power and the way these dimensions are associated with the topics discussed in eWOM, thus revealing patterns relating tourists’ experiences with potential explanations for their dissatisfaction/satisfaction. The method is implemented in a case study in the context of tourism in Cyprus focusing on two hotel groups (2/3 and 4/5 stars) to account for their differences. Patterns emerged from the extraction of rules from DTs illuminate combinations of variables associated with tourist experience (negative or positive) for each of the two hotel categories and verify the asymmetric relationship between service performance and satisfaction. The approach can be used by management during marketing campaigns to design messages to better address the desires and needs of tourists from different cultural and economic backgrounds, as these emerge from the data analysis.
Andreas Gregoriades; Maria Pampaka; Herodotos Herodotou; Evripides Christodoulou. Supporting digital content marketing and messaging through topic modelling and decision trees. Expert Systems with Applications 2021, 184, 115546 .
AMA StyleAndreas Gregoriades, Maria Pampaka, Herodotos Herodotou, Evripides Christodoulou. Supporting digital content marketing and messaging through topic modelling and decision trees. Expert Systems with Applications. 2021; 184 ():115546.
Chicago/Turabian StyleAndreas Gregoriades; Maria Pampaka; Herodotos Herodotou; Evripides Christodoulou. 2021. "Supporting digital content marketing and messaging through topic modelling and decision trees." Expert Systems with Applications 184, no. : 115546.
Artificial Neural Networks (ANNs) have wide applications in aquatic ecology and specifically in modelling water quality and biotic responses to environmental predictors. However, data scarcity is a common problem that raises the need to optimize modelling approaches to overcome data limitations. With this paper, we investigate the optimal k-fold cross validation in building an ANN using a small water-quality data set. The ANN was created to model the chlorophyll-a levels of a shallow eutrophic lake (Mikri Prespa) located in N. Greece. The typical water quality parameters serving as the ANN’s inputs are pH, dissolved oxygen, water temperature, phosphorus, nitrogen, electric conductivity, and Secchi disk depth. The available data set was small, containing only 89 data samples. For that reason, k-fold cross validation was used for training the ANN. To find the optimal k value for the k-fold cross validation, several values of k were tested (ranging from 3 to 30). Additionally, the leave-one-out (LOO) cross validation, which is an extreme case of the k-fold cross validation, was also applied. The ANN’s performance indices showed a clear trend to be improved as the k number was increased, while the best results were calculated for the LOO cross validation as expected. The computational times were calculated for each k value, where it was found the computational time is relatively low when applying the more expensive LOO cross validation; therefore, the LOO is recommended. Finally, a sensitivity analysis was examined using the ANN to investigate the interactions of the input parameters with the Chlorophyll-a, and hence examining the potential use of the ANN as a water management tool for nutrient control.
Ekaterini Hadjisolomou; Konstantinos Stefanidis; Herodotos Herodotou; Michalis Michaelides; George Papatheodorou; Eva Papastergiadou. Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks. Water 2021, 13, 1590 .
AMA StyleEkaterini Hadjisolomou, Konstantinos Stefanidis, Herodotos Herodotou, Michalis Michaelides, George Papatheodorou, Eva Papastergiadou. Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks. Water. 2021; 13 (11):1590.
Chicago/Turabian StyleEkaterini Hadjisolomou; Konstantinos Stefanidis; Herodotos Herodotou; Michalis Michaelides; George Papatheodorou; Eva Papastergiadou. 2021. "Modelling Freshwater Eutrophication with Limited Limnological Data Using Artificial Neural Networks." Water 13, no. 11: 1590.
Microgrids have recently emerged as the building block of a smart grid combining distributed renewable energy sources, energy storage devices, and load management in order to improve power system reliability, enhance sustainable development, and reduce carbon emissions
Herodotos Herodotou. Introduction to the Special Issue on Data-Intensive Computing in Smart Microgrids. Energies 2021, 14, 2704 .
AMA StyleHerodotos Herodotou. Introduction to the Special Issue on Data-Intensive Computing in Smart Microgrids. Energies. 2021; 14 (9):2704.
Chicago/Turabian StyleHerodotos Herodotou. 2021. "Introduction to the Special Issue on Data-Intensive Computing in Smart Microgrids." Energies 14, no. 9: 2704.
Automatic generation control (AGC) is primarily responsible for ensuring the smooth and efficient operation of an electric power system. The main goal of AGC is to keep the operating frequency under prescribed limits and maintain the interchange power at the intended level. Therefore, an AGC system must be supplemented with modern and intelligent control techniques to provide adequate power supply. This paper provides a comprehensive overview of various AGC models in diverse configurations of the power system. Initially, the history of power system AGC models is explored and the basic operation of AGC in a multi-area interconnected power system is presented. An in-depth analysis of various control methods used to mitigate the AGC issues is provided. Application of fast-acting energy storage devices, high voltage direct current (HVDC) interconnections, and flexible AC transmission systems (FACTS) devices in the AGC systems are investigated. Furthermore, AGC systems employed in different renewable energy generation systems are overviewed and are summarized in tabulated form. AGC techniques in different configurations of microgrid and smart grid are also presented in detail. A thorough overview of various AGC issues in a deregulated power system is provided by considering the different contract scenarios. Moreover, AGC systems with an additional objective of economic dispatch is investigated and an overview of worldwide AGC practices is provided. Finally, the paper concludes with an emphasis on the prospective study in the field of AGC.
Kaleem Ullah; Abdul Basit; Zahid Ullah; Sheraz Aslam; Herodotos Herodotou. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies 2021, 14, 2376 .
AMA StyleKaleem Ullah, Abdul Basit, Zahid Ullah, Sheraz Aslam, Herodotos Herodotou. Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview. Energies. 2021; 14 (9):2376.
Chicago/Turabian StyleKaleem Ullah; Abdul Basit; Zahid Ullah; Sheraz Aslam; Herodotos Herodotou. 2021. "Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview." Energies 14, no. 9: 2376.
Herodotos Herodotou; Panos K. Chrysanthis; Shimin Chen; Meichun Hsu; Khuzaima Daudjee; Yingjun Wu; Constantinos Costa. Introduction to the special issue on self‑managing and hardware‑optimized database systems 2020. Distributed and Parallel Databases 2021, 1 -3.
AMA StyleHerodotos Herodotou, Panos K. Chrysanthis, Shimin Chen, Meichun Hsu, Khuzaima Daudjee, Yingjun Wu, Constantinos Costa. Introduction to the special issue on self‑managing and hardware‑optimized database systems 2020. Distributed and Parallel Databases. 2021; ():1-3.
Chicago/Turabian StyleHerodotos Herodotou; Panos K. Chrysanthis; Shimin Chen; Meichun Hsu; Khuzaima Daudjee; Yingjun Wu; Constantinos Costa. 2021. "Introduction to the special issue on self‑managing and hardware‑optimized database systems 2020." Distributed and Parallel Databases , no. : 1-3.
Microgrids have recently emerged as a building block for smart grids combining distributed renewable energy sources (RESs), energy storage devices, and load management methodologies. The intermittent nature of RESs brings several challenges to the smart microgrids, such as reliability, power quality, and balance between supply and demand. Thus, forecasting power generation from RESs, such as wind turbines and solar panels, is becoming essential for the efficient and perpetual operations of the power grid and it also helps in attaining optimal utilization of RESs. Energy demand forecasting is also an integral part of smart microgrids that helps in planning the power generation and energy trading with commercial grid. Machine learning (ML) and deep learning (DL) based models are promising solutions for predicting consumers’ demands and energy generations from RESs. In this context, this manuscript provides a comprehensive survey of the existing DL-based approaches, which are developed for power forecasting of wind turbines and solar panels as well as electric power load forecasting. It also discusses the datasets used to train and test the different DL-based prediction models, enabling future researchers to identify appropriate datasets to use in their work. Even though there are a few related surveys regarding energy management in smart grid applications, they are focused on a specific production application such as either solar or wind. Moreover, none of the surveys review the forecasting schemes for production and load side simultaneously. Finally, previous surveys do not consider the datasets used for forecasting despite their significance in DL-based forecasting approaches. Hence, our survey work is intrinsically different due to its data-centered view, along with presenting DL-based applications for load and energy generation forecasting in both residential and commercial sectors. The comparison of different DL approaches discussed in this manuscript reveals that the efficiency of such forecasting methods is highly dependent on the amount of the historical data and thus a large number of data storage devices and high processing power devices are required to deal with big data. Finally, this study raises several open research problems and opportunities in the area of renewable energy forecasting for smart microgrids.
Sheraz Aslam; Herodotos Herodotou; Syed Muhammad Mohsin; Nadeem Javaid; Nouman Ashraf; Shahzad Aslam. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews 2021, 144, 110992 .
AMA StyleSheraz Aslam, Herodotos Herodotou, Syed Muhammad Mohsin, Nadeem Javaid, Nouman Ashraf, Shahzad Aslam. A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews. 2021; 144 ():110992.
Chicago/Turabian StyleSheraz Aslam; Herodotos Herodotou; Syed Muhammad Mohsin; Nadeem Javaid; Nouman Ashraf; Shahzad Aslam. 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids." Renewable and Sustainable Energy Reviews 144, no. : 110992.
SQL-on-Hadoop engines such as Hive provide a declarative interface for processing large-scale data over computing frameworks such as Hadoop. The underlying frameworks contain a large number of configuration parameters that can significantly impact performance, but which are hard to tune. The problem of automatic parameter tuning has become a lively research area and several sophisticated tuning advisors have been proposed for Hadoop. In this paper, we conduct an experimental study to explore the impact of Hadoop parameter tuning on Hive. We reveal that the performance of Hive queries does not necessarily improve when using Hadoop-focused tuning advisors out-of-the-box, at least when following the current approach of applying the same tuning setup uniformly for evaluating the entire query. After extending the Hive query processing engine, we propose an alternative tuning approach and experimentally show how current Hadoop tuning advisors can now provide good and robust performance for Hive queries, as well as improved cluster resource utilization. We share our observations with the community and hope to create an awareness for this problem as well as to initiate new research on automatic parameter tuning for SQL-on-Hadoop systems.
Edson Ramiro Lucas Filho; Eduardo Cunha de Almeida; Stefanie Scherzinger; Herodotos Herodotou. Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems. Big Data Research 2021, 25, 100204 .
AMA StyleEdson Ramiro Lucas Filho, Eduardo Cunha de Almeida, Stefanie Scherzinger, Herodotos Herodotou. Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems. Big Data Research. 2021; 25 ():100204.
Chicago/Turabian StyleEdson Ramiro Lucas Filho; Eduardo Cunha de Almeida; Stefanie Scherzinger; Herodotos Herodotou. 2021. "Investigating Automatic Parameter Tuning for SQL-on-Hadoop Systems." Big Data Research 25, no. : 100204.
The maritime domain encompasses a diverse set of heterogeneous large-scale data about ships, routes and trajectories, port operations, fishing and maritime biodiversity, oceans, and environmental conditions. Performing timely and cost-effective analytical processing of this data is a key priority for maritime stakeholders in order to extract deep insights and automate various decision-making processes that will lead to optimising marine transport, improving fuel efficiency, and optimising port operational efficiency among others. The maritime data value chain defines the series of activities needed to appropriately manage data during the entire life-cycle of data as well as to extract value and useful insights from maritime data. The four key activities identified are: (1) data acquisition for collecting the data across different and geographically-dispersed data sources; (2) data pre-processing for transforming, integrating, and assessing the quality of the data; (3) data storage for storing data in a persistent and scalable way; and (4) data usage for processing the data and extracting value. This chapter provides an extensive overview of the maritime data value chain and discusses state-of-the-art technological solutions for managing and processing maritime data in efficient and effective ways.
Herodotos Herodotou; Sheraz Aslam; Henrik Holm; Socrates Theodossiou. Big Maritime Data Management. Progress in IS 2020, 313 -334.
AMA StyleHerodotos Herodotou, Sheraz Aslam, Henrik Holm, Socrates Theodossiou. Big Maritime Data Management. Progress in IS. 2020; ():313-334.
Chicago/Turabian StyleHerodotos Herodotou; Sheraz Aslam; Henrik Holm; Socrates Theodossiou. 2020. "Big Maritime Data Management." Progress in IS , no. : 313-334.
The recent emergence of Internet of Things (IoT) technologies in mission-critical applications in the maritime industry has led to the introduction of the Internet of Ships (IoS) paradigm. IoS is a novel application domain of IoT that refers to the network of smart interconnected maritime objects, which can be any physical device or infrastructure associated with a ship, a port, or the transportation itself, with the goal of significantly boosting the shipping industry towards improved safety, efficiency, and environmental sustainability. In this manuscript, we provide a comprehensive survey of the IoS paradigm, its architecture, its key elements, and its main characteristics. Furthermore, we review the state of the art for its emerging applications, including safety enhancements, route planning and optimization, collaborative decision making, automatic fault detection and preemptive maintenance, cargo tracking, environmental monitoring, energy-efficient operations, and automatic berthing. Finally, the presented open challenges and future opportunities for research in the areas of satellite communications, security, privacy, maritime data collection, data management, and analytics, provide a road-map towards optimized maritime operations and autonomous shipping.
Sheraz Aslam; Michalis P. Michaelides; Herodotos Herodotou. Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal 2020, 7, 9714 -9727.
AMA StyleSheraz Aslam, Michalis P. Michaelides, Herodotos Herodotou. Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges. IEEE Internet of Things Journal. 2020; 7 (10):9714-9727.
Chicago/Turabian StyleSheraz Aslam; Michalis P. Michaelides; Herodotos Herodotou. 2020. "Internet of Ships: A Survey on Architectures, Emerging Applications, and Challenges." IEEE Internet of Things Journal 7, no. 10: 9714-9727.
Data-intensive platforms such as Hadoop and Spark are routinely used to process massive amounts of data residing on distributed file systems like HDFS. Increasing memory sizes and new hardware technologies (e.g., NVRAM, SSDs) have recently led to the introduction of storage tiering in such settings. However, users are now burdened with the additional complexity of managing the multiple storage tiers and the data residing on them while trying to optimize their workloads. In this paper, we develop a general framework for automatically moving data across the available storage tiers in distributed file systems. Moreover, we employ machine learning for tracking and predicting file access patterns, which we use to decide when and which data to move up or down the storage tiers for increasing system performance. Our approach uses incremental learning to dynamically refine the models with new file accesses, allowing them to naturally adjust and adapt to workload changes over time. Our extensive evaluation using realistic workloads derived from Facebook and CMU traces compares our approach with several other policies and showcases significant benefits in terms of both workload performance and cluster efficiency.
Herodotos Herodotou; Elena Kakoulli. Automating Distributed Tiered Storage Management in Cluster Computing. 2019, 1 .
AMA StyleHerodotos Herodotou, Elena Kakoulli. Automating Distributed Tiered Storage Management in Cluster Computing. . 2019; ():1.
Chicago/Turabian StyleHerodotos Herodotou; Elena Kakoulli. 2019. "Automating Distributed Tiered Storage Management in Cluster Computing." , no. : 1.
The sustainability of Short Sea Shipping (SSS) is central to a clean, safe, and efficient European Union (EU) transport system. We report on key challenges for advancing reliability, quality, and safety, and removing unnecessary costs and delays at SSS hubs, with a particular focus on Cyprus and the Eastern Mediterranean. Specifically, we consider the effect of port-2-port (P2P) communication on port efficiency by investigating the factors influencing the various waiting times at the Port of Limassol, both from a qualitative and a quantitative perspective. The qualitative results are based on the views of key stakeholders involved in the port call process. The quantitative analysis relies on data from over 8000 port calls during 2017–2018, which are analyzed with respect to ship type, port of origin, and shipping agent. The calculated Key Performance Indicators (KPIs) include arrival punctuality, berth waiting, and berth utilization. The analysis clearly reveals considerable variation in agent performance regarding the KPIs, suggesting a lack of attention to the social aspect of a port’s socio-technical system. We propose measures for improving agent performance based on the principles of Port Collaborative Decision Making (PortCDM), including P2P communication, data sharing and transparency among all involved in a port call process including the agents, and open dissemination of agent-specific KPIs.
Michalis P. Michaelides; Herodotos Herodotou; Mikael Lind; Richard T. Watson. Port-2-Port Communication Enhancing Short Sea Shipping Performance: The Case Study of Cyprus and the Eastern Mediterranean. Sustainability 2019, 11, 1912 .
AMA StyleMichalis P. Michaelides, Herodotos Herodotou, Mikael Lind, Richard T. Watson. Port-2-Port Communication Enhancing Short Sea Shipping Performance: The Case Study of Cyprus and the Eastern Mediterranean. Sustainability. 2019; 11 (7):1912.
Chicago/Turabian StyleMichalis P. Michaelides; Herodotos Herodotou; Mikael Lind; Richard T. Watson. 2019. "Port-2-Port Communication Enhancing Short Sea Shipping Performance: The Case Study of Cyprus and the Eastern Mediterranean." Sustainability 11, no. 7: 1912.
The continuous improvements in memory, storage devices, and network technologies of commodity hardware introduce new challenges and opportunities in tiered storage management. Whereas past work is exploiting storage tiers in pairs or for specific applications, OctopusFS---a novel distributed file system that is aware of the underlying storage media---offers a comprehensive solution to managing multiple storage tiers in a distributed setting. OctopusFS contains auto-mated data-driven policies for managing the placement and retrieval of data across the nodes and storage tiers of the cluster. It also exposes the network locations and storage tiers of the data in order to allow higher-level systems to make locality-aware and tier-aware decisions. This demonstration will showcase the web interface of OctopusFS, which enables users to (i) view detailed utilization information for the various storage tiers and nodes, (ii) browse the directory namespace and perform file-related actions, and (iii) execute caching-related operations while observing their performance impact on MapReduce and Spark workloads.
Elena Kakoulli; Nikolaos D. Karmiris; Herodotos Herodotou. OctopusFS in action. Proceedings of the VLDB Endowment 2018, 11, 1914 -1917.
AMA StyleElena Kakoulli, Nikolaos D. Karmiris, Herodotos Herodotou. OctopusFS in action. Proceedings of the VLDB Endowment. 2018; 11 (12):1914-1917.
Chicago/Turabian StyleElena Kakoulli; Nikolaos D. Karmiris; Herodotos Herodotou. 2018. "OctopusFS in action." Proceedings of the VLDB Endowment 11, no. 12: 1914-1917.
The uptake of wearable technology suggests that the time is ripe to explore new opportunities for improving mobile experiences. Apps, however, are not keeping up with the pace of technological advancement because wearables are treated as standalone devices, although their individual capabilities better classify them as peripherals with complementary roles. We foresee that the next generation of apps will orchestrate multiple wearable devices to enhance mobile user experiences. However, currently there is limited support for combining heterogeneous devices. This paper introduces Multi-Wear, a platform to scaffold the development of apps that span multiple wearables. It demonstrates experimentally how MULTI-WEAR can help bring changes to mobile apps that go beyond conventional practices.
Andreas Pamboris; Panayiotis Andreou; Herodotos Herodotou; George Samaras. MULTI-WEAR: A multi-wearable platform for enhancing mobile experiences. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) 2018, 1 -6.
AMA StyleAndreas Pamboris, Panayiotis Andreou, Herodotos Herodotou, George Samaras. MULTI-WEAR: A multi-wearable platform for enhancing mobile experiences. 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC). 2018; ():1-6.
Chicago/Turabian StyleAndreas Pamboris; Panayiotis Andreou; Herodotos Herodotou; George Samaras. 2018. "MULTI-WEAR: A multi-wearable platform for enhancing mobile experiences." 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC) , no. : 1-6.
The ever-growing data storage and I/O demands of modern large-scale data analytics are challenging the current distributed storage systems. A promising trend is to exploit the recent improvements in memory, storage media, and networks for sustaining high performance and low cost. While past work explores using memory or SSDs as local storage or combine local with network-attached storage in cluster computing, this work focuses on managing multiple storage tiers in a distributed setting. We present OctopusFS, a novel distributed file system that is aware of heterogeneous storage media (e.g., memory, SSDs, HDDs, NAS) with different capacities and performance characteristics. The system offers a variety of pluggable policies for automating data management across the storage tiers and cluster nodes. The policies employ multi-objective optimization techniques for making intelligent data management decisions based on the requirements of fault tolerance, data and load balancing, and throughput maximization. At the same time, the storage media are explicitly exposed to users and applications, allowing them to choose the distribution and placement of replicas in the cluster based on their own performance and fault tolerance requirements. Our extensive evaluation shows the immediate benefits of using OctopusFS with data-intensive processing systems, such as Hadoop and Spark, in terms of both increased performance and better cluster utilization.
Elena Kakoulli; Herodotos Herodotou. OctopusFS. Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video 2017, 65 -78.
AMA StyleElena Kakoulli, Herodotos Herodotou. OctopusFS. Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video. 2017; ():65-78.
Chicago/Turabian StyleElena Kakoulli; Herodotos Herodotou. 2017. "OctopusFS." Proceedings of the 2017 ACM International Conference on Interactive Experiences for TV and Online Video , no. : 65-78.
The amount of data collected by modern industrial, government, and academic organizations has been increasing exponentially and will continue to grow at an accelerating rate for the foreseeable future. At companies across all industries, servers are overflowing with usage logs, message streams, transaction records, sensor data, business operations records, and mobile device data. Effectively analyzing these massive collections of data (“big data”) can create significant value for the world economy by enhancing productivity, increasing efficiency, and delivering more value to consumers. The need to convert raw data into useful information has led to the development of advanced and unique data storage, management, analysis, and visualization technologies, especially over the last decade. This monograph is an attempt to cover the design principles and core features of systems for analyzing very large datasets for business purposes. In particular, we organize systems into four main categories based on major and distinctive technological innovations. Parallel databases dating back to 1980s have added techniques like columnar data storage and processing, while new distributed platforms like MapReduce have been developed. Other innovations aimed at creating alternative system architectures for more generalized dataflow applications. Finally, the growing demand for interactive analytics has led to the emergence of a new class of systems that combine analytical and transactional capabilities.
Herodotos Herodotou. Business Intelligence and Analytics: Big Systems for Big Data. Analytics, Innovation, and Excellence-Driven Enterprise Sustainability 2017, 7 -49.
AMA StyleHerodotos Herodotou. Business Intelligence and Analytics: Big Systems for Big Data. Analytics, Innovation, and Excellence-Driven Enterprise Sustainability. 2017; ():7-49.
Chicago/Turabian StyleHerodotos Herodotou. 2017. "Business Intelligence and Analytics: Big Systems for Big Data." Analytics, Innovation, and Excellence-Driven Enterprise Sustainability , no. : 7-49.
The proliferation of wearable and smartphone devices with embedded sensors has enabled researchers and engineers to study and understand user behavior at an extremely high fidelity, particularly for use in industries such as entertainment, health, and retail. However, identified user patterns are yet to be integrated into modern systems with immersive capabilities, such as VR systems, which still remain constrained by limited application interaction models exposed to developers. In this paper, we present Smart VR, a platform that allows developers to seamlessly incorporate user behavior into VR apps. We present the high-level architecture of Smart VR, and show how it facilitates communication, data acquisition, and context recognition between smart wearable devices and mediator systems (e.g., smartphones, tablets, PCs). We demonstrate Smart VR in the context of a VR app for retail stores to show how it can be used to substitute the requirement of cumbersome input devices (e.g., mouse, keyboard) with more natural means of user-app interaction (e.g., user gestures such as swiping and tapping) to improve user experience.
Salah Eddin Alshaal; Stylianos Michael; Andreas Pamporis; Herodotos Herodotou; George Samaras; Panayiotis Andreou. Enhancing Virtual Reality Systems with Smart Wearable Devices. 2016 17th IEEE International Conference on Mobile Data Management (MDM) 2016, 1, 345 -348.
AMA StyleSalah Eddin Alshaal, Stylianos Michael, Andreas Pamporis, Herodotos Herodotou, George Samaras, Panayiotis Andreou. Enhancing Virtual Reality Systems with Smart Wearable Devices. 2016 17th IEEE International Conference on Mobile Data Management (MDM). 2016; 1 ():345-348.
Chicago/Turabian StyleSalah Eddin Alshaal; Stylianos Michael; Andreas Pamporis; Herodotos Herodotou; George Samaras; Panayiotis Andreou. 2016. "Enhancing Virtual Reality Systems with Smart Wearable Devices." 2016 17th IEEE International Conference on Mobile Data Management (MDM) 1, no. : 345-348.
Distributed storage systems running on clusters of commodity hardware are challenged by the ever-growing data storage and I/O demands of modern large-scale data analytics. A promising trend is to exploit the recent improvements in memory, storage media, and network technologies for sustaining high performance at low cost. While recent work explores using memory and SSDs as a cache for local storage or combining local with network-attached storage, no work has ever looked at all layers together in a distributed setting. We present a novel design for a distributed file system that is aware of heterogeneous storage media (e.g., memory, SSDs, HDDs, NAS) with different capacities and performance characteristics. The storage media are explicitly exposed to users and applications, allowing them to choose the distribution and placement of replicas in the cluster based on their own performance and fault tolerance requirements. At the same time, the system offers a variety of pluggable policies for automating data management for increased performance and better cluster utilization. We analyze the new trends and challenges that led to our application- and data-centric design choices, and discuss how those choices inspire new research opportunities for data-intensive processing systems.
Herodotos Herodotou. Towards a distributed multi-tier file system for cluster computing. 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) 2016, 131 -134.
AMA StyleHerodotos Herodotou. Towards a distributed multi-tier file system for cluster computing. 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW). 2016; ():131-134.
Chicago/Turabian StyleHerodotos Herodotou. 2016. "Towards a distributed multi-tier file system for cluster computing." 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) , no. : 131-134.
Herodotos Herodotou; Bolin Ding; Shobana Balakrishnan; Geoff Outhred; Percy Fitter. Scalable near real-time failure localization of data center networks. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining 2014, 1689 -1698.
AMA StyleHerodotos Herodotou, Bolin Ding, Shobana Balakrishnan, Geoff Outhred, Percy Fitter. Scalable near real-time failure localization of data center networks. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014; ():1689-1698.
Chicago/Turabian StyleHerodotos Herodotou; Bolin Ding; Shobana Balakrishnan; Geoff Outhred; Percy Fitter. 2014. "Scalable near real-time failure localization of data center networks." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining , no. : 1689-1698.
There is a growing trend of performing analysis on large datasets using workflows composed of MapReduce jobs connected through producer-consumer relationships based on data. This trend has spurred the development of a number of interfaces---ranging from program-based to query-based interfaces---for generating MapReduce workflows. Studies have shown that the gap in performance can be quite large between optimized and unoptimized workflows. However, automatic cost-based optimization of MapReduce workflows remains a challenge due to the multitude of interfaces, large size of the execution plan space, and the frequent unavailability of all types of information needed for optimization. We introduce a comprehensive plan space for MapReduce workflows generated by popular workflow generators. We then propose Stubby , a cost-based optimizer that searches selectively through the subspace of the full plan space that can be enumerated correctly and costed based on the information available in any given setting. Stubby enumerates the plan space based on plan-to-plan transformations and an efficient search algorithm. Stubby is designed to be extensible to new interfaces and new types of optimizations, which is a desirable feature given how rapidly MapReduce systems are evolving. Stubby's efficiency and effectiveness have been evaluated using representative workflows from many domains.
Harold Lim; Herodotos Herodotou; Shivnath Babu. Stubby. Proceedings of the VLDB Endowment 2012, 5, 1196 -1207.
AMA StyleHarold Lim, Herodotos Herodotou, Shivnath Babu. Stubby. Proceedings of the VLDB Endowment. 2012; 5 (11):1196-1207.
Chicago/Turabian StyleHarold Lim; Herodotos Herodotou; Shivnath Babu. 2012. "Stubby." Proceedings of the VLDB Endowment 5, no. 11: 1196-1207.
Publishers of Foundations and Trends, making research accessible
Shivnath Babu; Herodotos Herodotou. Massively Parallel Databases and MapReduce Systems. Foundations and Trends® in Databases 2012, 5, 1 -104.
AMA StyleShivnath Babu, Herodotos Herodotou. Massively Parallel Databases and MapReduce Systems. Foundations and Trends® in Databases. 2012; 5 (1):1-104.
Chicago/Turabian StyleShivnath Babu; Herodotos Herodotou. 2012. "Massively Parallel Databases and MapReduce Systems." Foundations and Trends® in Databases 5, no. 1: 1-104.