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Evidence Based Big Data Benchmarking to Improve Business Performance

Risultati finali

DataBench Benchmarking Handbook

DataBench Benchmarking Handbook will be a handbook describing the development, organisation and use of main industrial and business performance benchmarks targeted at industrial users and European technology developers.

DataBench Architecture

DataBench Architecture will describe the DataBench Toolbox architecture.

Final evaluation of DataBench metrics

The Final evaluation of DataBench metrics will present a final set of indicators for comparing different datasets that will allow to structure the metrics and the data collections according to different characteristics and provide a final landscape.

Horizontal Benchmarks – Analytics and Processing

Horizontal Benchmarks – Analytics and Processing presents the incremental updates of the various horizontal benchmarks in the area of analytics and processing.

Evaluation of business performance

Evaluation of business performance will present the results of the evaluation of business process performance based on evidence from data collection activities.

Initial Evaluation of DataBench Metrics

Initial Evaluation of DataBench Metrics will present an initial set of indicators that will allow to structure the metrics and data collections according to different characteristics and provide a landscape prototype.

Assessment of technical usability, relevance, scale and complexity

The Assessment of technical usability, relevance, scale and complexity Deliverable will provide initial assessment of technical usability, relevance, scalability and complexity of Big-Data tasks.

Final report on methodology for evaluation of industrial analytic projects scenarios

Final report on methodology for evaluation of industrial analytic projects scenarios will provide the results and assessment of Big-Data projects according to the DataBench methodology and a description of how to use the methodologies for industrial evaluation analytics.

Preliminary Benchmarks of European and industrial significance

Preliminary Benchmarks of European and industrial significance will provide a first assessment of the value and economic relevance of the EU industry sectors using and developing BDT technologies and of the industrial significance of the performance parameters measured by the BDT benchmarks.

Benchmarks of European and industrial significance

Benchmarks of European and industrial significance will present the final benchmarks of European and industrial significance drawing from the results of task 2.3 “Analysis of actual and emerging needs of industrial users” and, feeding into the exploitation plan and the dissemination deliverables. Together with the Benchmarking Handbook this deliverable will provide the evidence-based methods needed to demonstrate the success of BDVA-driven technologies.

Dissemination and Liaison plan

This Deliverable will provide a detailed schedule of the communication and dissemination activities planned, their targets and the communication channels, as well as the Liaison plan to establish working relationship with the ICT 15 and 15 projects and other relevant projects/communities.

Data collection plan

Data collection plan will present a detailed plan of data collection activities, industries and projects considered, information sources and data collection methodologies.

Analytic modelling relationships between metrics, data and project methodologies

This descriptive document and related native format schematic materials will provide the mapping between metrics, data characteristics and methodologies. Every metric and dataset used within benchmarking will be associated with tasks and problems and initial set of complexity and scalability indicators will be identified.

Industry Requirements with benchmark metrics and KPIs

This Deliverable will document the collected industrial requirements of European significance with mappings to related vertical and horizontal benchmark metrics and KPIs.

Release Version of DataBench Toolbox including visualization and search components

Release Version of DataBench Toolbox including visualization and search components will present the specification of the release version of DataBench Toolbox. The focus will be on the components defined in the layered architecture of the platform emphasizing on the visual components.

Horizontal Benchmarks – Data Management

Horizontal Benchmarks – Data Management presents the incremental updates of the various horizontal benchmarks in the area of data management, including data acquisition and curation and data storage for various classes of storage systems.

Project Fiche

General description of the project, partners and plan for public dissemination.

Dissemination and Liaison report period 1

This Deliverable will describe the results of the dissemination and liaison activities in the first 18 months of the project and will provide an update of these activities planned for period 2. It will contain record of all events, dissemination materials, scientific papers and links to web based content.

Dissemination and Liaison report period 2

This Deliverable will describe the results of the dissemination and liaison activities in the second 18 month period of the project and will provide an overview of major achievements during the project. It will contain record of all events, dissemination materials, scientific papers and links to web based content.

Analysis of actual and emerging needs of industrial users and use case mapping

Analysis of actual and emerging needs of industrial users and use case mapping will report the results of the analysis of the actual and emerging needs of industrial users of BD, based on IDC data and an ad-hoc survey of pioneer industrial users (running pilot cases in the ICT14 and ICT 15 H2020 projects) and other users.

DataBench Toolbox - Beta including end-to-end-scenario tool

DataBench Toolbox - Beta including end-to-end-scenario tool (M24) will present the specification about beta version of DataBench Toolbox. The focus will be on the implementation of the back-end of the DataBench Toolbox and associated tools.

DataBench Toolbox – Alpha including support for reusing of existing benchmarks

DataBench Toolbox – Alpha including support for reusing of existing benchmarks will present the specification about alpha version of DataBench Toolbox. The focus will be on the components that will allow the reuse of existing benchmarks resources in the platform.

Project Web Portal and Dissemination Materials

Project Web Portal and Dissemination Materials Will launch the project online presence and website and provide the dissemination materials kit.

Data collection results

Data collection results will describe the results of data collection activities presenting the data that will be used to work with and validate the DataBench methodology.

Pubblicazioni

Building the DataBench Workflow and Architecture

Autori: Todor Ivanov, Timo Eichhorn, Arne Jørgen Berre, Tomás Pariente Lobo, Ivan Martinez Rodriguez, Ricardo Ruiz Saiz, Barbara Pernici, Chiara Francalanci
Pubblicato in: Benchmarking, Measuring, and Optimizing - Second BenchCouncil International Symposium, Bench 2019, Denver, CO, USA, November 14–16, 2019, Revised Selected Papers, Numero 12093, 2020, Pagina/e 165-171, ISBN 978-3-030-49555-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-030-49556-5_17

Analytics Benchmarks

Autori: Todor Ivanov, Roberto V. Zicari
Pubblicato in: Encyclopedia of Big Data Technologies, Numero 10 March 2018, 2018, Pagina/e 1-10, ISBN 978-3-319-63962-8
Editore: Springer International Publishing
DOI: 10.1007/978-3-319-63962-8_113-1

KPIs of BDA Business Impacts in Manufacturing

Autori: Gabriella Cattaneo, Sergio Gusmeroli
Pubblicato in: Big Data Challenges in Smart Manufacturing Industry V. 2020, Numero July 2020, 2020, Pagina/e 8
Editore: Big Data Value Association (BDVA)

CoreBigBench - Benchmarking big data core operations

Autori: Todor Ivanov, Ahmad Ghazal, Alain Crolotte, Pekka Kostamaa, Yoseph Ghazal
Pubblicato in: Proceedings of the workshop on Testing Database Systems, 2020, Pagina/e 1-6, ISBN 9781450380010
Editore: ACM
DOI: 10.1145/3395032.3395324

Tutorial on Benchmarking Big Data Analytics Systems

Autori: Todor Ivanov, Rekha Singhal
Pubblicato in: Companion of the ACM/SPEC International Conference on Performance Engineering, 2020, Pagina/e 50-53, ISBN 9781450371094
Editore: ACM
DOI: 10.1145/3375555.3383121

Relating Big Data Business and Technical Performance Indicators

Autori: Pernici, Barbara; Francalanci, Chiara; Geronazzo, Angela; Lucia, Polidori; Stefano, Ray; Leonardo, Riva; Arne Jørgen Berre,; Todor, Ivanov
Pubblicato in: XV Conference of the Italian Chapter of AIS, Numero 2018, 2018
Editore: -

Adding Velocity to BigBench

Autori: Todor Ivanov, Patrick Bedué, Ahmad Ghazal, Roberto V. Zicari
Pubblicato in: Proceedings of the Workshop on Testing Database Systems - DBTest'18, Numero 15 June 2018, 2018, Pagina/e 1-6, ISBN 9781-450358262
Editore: ACM Press
DOI: 10.1145/3209950.3209956

Exploratory Analysis of Spark Structured Streaming

Autori: Todor Ivanov, Jason Taafe
Pubblicato in: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering - ICPE '18, Numero 2018, 2018, Pagina/e 141-146, ISBN 9781-450356299
Editore: ACM Press
DOI: 10.1145/3185768.3186360

ABench - Big Data Architecture Stack Benchmark

Autori: Todor Ivanov, Rekha Singhal
Pubblicato in: Companion of the 2018 ACM/SPEC International Conference on Performance Engineering - ICPE '18, Numero 2018, 2018, Pagina/e 13-16, ISBN 9781-450356299
Editore: ACM Press
DOI: 10.1145/3185768.3186300

DataBench: Evidence Based Big Data Benchmarking to Improve Business Performance

Autori: Richard Stevens, Gabriella Cattaneo, Helena Schwenk, Cristina Pepato, Christopher Ostberg Hansen, Nuria De Lama Sanchez, Tomas Pariente Lobo, Chiara Francalanci, Barbara Pernici, Angela Geronazzo, Paolo Giacomazzi, Lucia Polidori, Arne Jørgen Berre, Volker Hoffman, Marko Grobelnik, James Hodson, and Todor Ivanov, Roberto V. Zicari
Pubblicato in: Proceedings of The 24th ACM SIGKDD International Conference on Knowledge Discovery Data Mining (KDD’18), 2018, ISBN 978-1-4503-5552-0
Editore: ACM

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