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High-performance data-centric stack for big data applications and operations

Deliverables

Conceptual model and Reference architecture - II

This report will identify the key components of BigDataStack and define the interfaces and interactions between them. An internal report will be developed in M3 and will serve as a basis to kick-off the research activities of the project. The deliverables will also include the performance targets against which evaluation of outcomes will be performed following the SotA analysis.

WP 4 Scientific Report and Prototype Description - Y1

This series of reports will detail the scientific progress of tasks in this work package as well as describe the prototype code developed in this work period.

WP 3 Scientific Report and Prototype description - Y1

This series of reports will detail the scientific progress of tasks in this work package as well as describe the prototype code developed in this work period.

WP 5 Scientific Report and Prototype Description - Y1

This series of reports will detail the scientific progress of tasks in this work package as well as describe the prototype code developed in this work period.

Initial publication package

This deliverable will focus on the publication of the initial set of materials that will define and promote project’s identity. It will include the creation of a project logo, a project factsheet, an MS PowerPoint presentation providing a general description of BigDataStack, project’s official web site and templates for the official documents to be developed within the project.

Exploitation plan and business potential - M18

This deliverable releases the exploitation plans including the overall joint approach to the results exploitation as well as the partners` individual exploitation plans describing their intentions for exploitation of BigDataStack outcomes, detail their plans to achieve the targeted exploitation goals and report progress on planned actions. Additionally, this deliverable will examine the business potential for BigDataStack by looking at the exploitable assets of the project and the potential for them to be exploited in joint or individual fashion.

Conceptual model and Reference architecture - I

This report will identify the key components of BigDataStack and define the interfaces and interactions between them. An internal report will be developed in M3 and will serve as a basis to kick-off the research activities of the project. The deliverables will also include the performance targets against which evaluation of outcomes will be performed following the SotA analysis.

Innovation Potential: Initial Plan and Activities

This report will contain the list of the key features identified, to make the BigDataStack framework innovative with respect to competing solutions. Furthermore, it will describe what is the plan defined to achieve these goals and the activities done during the project duration to follow this plan.

State of the art and Requirements analysis - I

This report will examine the SotA for the technologies involved in BigDataStack, present possible future trends and analyse the identified both use case and technical requirements. In M3 an internal deliverable will be provided in order to provide input to the initial conceptual model and architecture. During the course of the project the technologies and requirements related to BigDataStack will continue being investigated in order to ensure that the objectives and innovations of the project are valid, work is performed taking into account the latest SotA and developments fulfil the identified goals and requirements.

Use case description and implemement - M18

This report will describe the use cases shown in the project demonstration, as well as describing the testbed configuration.

Dissemination and Standardisation

This report describes the work done for Dissemination and Standardisation in the project, representing tasks 7.2-7.3

Requirements & State of the Art Analysis - II

This report will examine the SotA for the technologies involved in BigDataStack, present possible future trends and analyse the identified both use case and technical requirements. In M3 an internal deliverable will be provided in order to provide input to the initial conceptual model and architecture. During the course of the project the technologies and requirements related to BigDataStack will continue being investigated in order to ensure that the objectives and innovations of the project are valid, work is performed taking into account the latest SotA and developments fulfil the identified goals and requirements.

Project Management Plan

Project Management - Describes the project management structure, procedures for communication, documentation, deliverables review, procedures to control project progress, and risk management.

Data Management Plan

BigDataStack will participate in the Pilot on Open Research Data in H2020 and will endeavour to offer open access to its scientific results reported in publications, to the relevant scientific data and to data generated during the course of the project. The plan will identify the best practices and specific standards for the generated data and assess their suitability for sharing and reuse in accordance with official guidelines.

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Publications

Integration of Mobility Data with Weather Information

Author(s): Nikolaos Koutroumanis Georgios M. Santipantakis Apostolos Glenis Christos Doulkeridis George A. Vouros
Published in: EDBT/ICDT 2019 Joint Conference, 2019
DOI: 10.5281/zenodo.2563133

BigDataStack: A Holistic Data-Driven Stack for Big Data Applications and Operations

Author(s): Dimosthenis Kyriazis, Christos Doulkeridis, Panagiotis Gouvas, Ricardo Jimenez-Peris, Ana Juan Ferrer, Leonidas Kallipolitis, Pavlos Kranas, George Kousiouris, Craig Macdonald, Richard McCreadie, Yosef Moatti, Apostolos Papageorgiou, Marta Patino-Martinez, Stathis Plitsos, Dimitris Poulopoulos, Antonio Paradell, Amaryllis Raouzaiou, Paula Ta-Shma, Valerio Vianello
Published in: 2018 IEEE International Congress on Big Data (BigData Congress), 2018, Page(s) 237-241
DOI: 10.1109/bigdatacongress.2018.00041

Reinforcement Learning Based Orchestration for Elastic Services

Author(s): Fadel Argerich, M., Cheng B., Fürst, J
Published in: 2019 IEEE 5th World Forum on Internet of Things, 2019

Big data skipping in the cloud

Author(s): Oshrit Feder, Guy Khazma, Gal Lushi, Yosef Moatti, Paula Ta-Shma
Published in: Proceedings of the 12th ACM International Conference on Systems and Storage - SYSTOR '19, 2019, Page(s) 193-193
DOI: 10.1145/3319647.3325854

RedHat Summit 2019

Author(s): Dimosthenis Kyriazis
Published in: Upcoming blog item on insights Researcher Day RedHat Summit, 2019

Fog Function: Serverless Fog Computing for Data Intensive IoT Services

Author(s): Bin Cheng, Jonathan Fuerst, Gurkan Solmaz, Takuya Sanada
Published in: 2019 IEEE INTERNATIONAL CONFERENCE ON SERVICES COMPUTING (SCC’19), 2019