More than ever, reducing the cost of data integration by efficiently evaluating queries is an important challenge, given that today the economic cost in computing cycles (see your cloud invoice); and in energy consumption and the performance required for some critical tasks have become important. Besides, new applications require solving even more complex queries including millions of sources, and data with high levels of volume and variety. These new challenges call for intelligent processes that can learn from previous experiences, that can be adaptable to changing requirements and dynamic execution contexts.

This workshop follows a first successful edition in conjunction with ICSOC 2019 in Toulouse that led to a vivid program with interesting discussion ( STRAPS 2019). The second edition of the workshop STRAPS aims at promoting scientific discussion on the way data produced under different conditions can be efficiently integrated to answer simple, relational, analytical queries. These queries must cope to quality preferences associated with providers, algorithms and data trust. New scales in volume, velocity and value associated with integrated data collections require adapted solutions providing computing, storage and processing services potentially deployed on different highly distributed infrastructures and target architectures. With service, data and algorithms stemming from different and potentially huge numbers of providers, properties like provenance, quality and trust, arise as key properties to be quantified, evaluated and exposed to data consumers. How can data integration in such conditions be smart? This is the key question to be discussed by workshop participants.

Underlying approaches and algorithms continue to evolve particularly given the new levels of volume, velocity and value associated with data. Contemporary infrastructures for dealing with data are deployed in heterogeneous target architectures like cloud, multi-cloud, Internet of Things consisting of sensors and server farms deployed around the world. These infrastructures go beyond classic data management solutions and evolve towards different stacks configurations (data processing systems “a la carte”).  They need to cope with new notions of scalability and of resources consumption guided by economic models, service level agreements and other quality warranties. 

OBJECTIVE

The workshop STRAPS aims at promoting scientific discussion on the way data stemming from different providers and produced under different conditions can be efficiently integrated to answer simple, relational, analytical queries ensuring providers, algorithms and data trust.

We invite the submission of work-in-progress research addressing various aspects of data integration and processing done in service-based infrastructures. The workshop welcomes submissions of technical, experimental, methodological papers, application papers, and papers on experience reports in real-life application settings addressing – though not limited to – the following topics:

• SLA models for data integration/data management
• Service-based data processing and querying
• Data services composition on cloud and multi-cloud
• Trust and privacy cloud services
• Data and service provenance
• Data quality: evaluation, estimation, warranties
• Microservices based data integration systems
• Polystores: architecture, querying and systems
• Learning-based data integration
• Logic-based data integration
• Large-scale data integration
• Data integration for smart applications
• Innovative data integration platforms
• Automated data integration
• Data integration strategies, Data integration management
• Data Cleaning, Curation, Filtering and Dissemination, Metadata Management, DataDiscovery, Web Data Integration, Semantic Web, Heterogeneous and Federated data sources
• Context-aware data integration
• Blockchain and data integration

We expect papers written in English between 8 and 12 Springer’s LNCS pages long ( Springer LNCS format), including references and Illustrations. Electronic submissions in PDF format can be proposed at the conference workshops  submission site.

Submission of a paper should be regarded as an undertaking that, should the paper be accepted, at least one of the authors will register and attend the workshop to present the paper. Registration is subject to the terms and conditions of  ICSOC.

A selected number of papers will be invited to be submitted as extended versions to be published in an edition of Proceedings series «  Advances in Computer Science Research ».

IMPORTANT DATES

Submission deadlines : 

Given that the general conference has gone officially online, organisers have authorised a longer period of submission for workshops. Therefore we have decided to organise 3 submission rounds with the following deadlines:

- 1 round: Submission: 5th September Notification of results: 1st October

- 2 round: Submission: 5th October Notification of results: 25th October

- 3 round: Submission: 30th October Notification of results: 15th November

Papers submitted in the first and second rounds, if not accepted, will have to opportunity to be submitted again in the following rounds. Authors wiil be able to integrate evaluation comments from reviewers to prepare a new submission.

• Camera Ready: 30 November 2020 
• Authors Registration deadline: 30 November 2020
• Workshops: December 14, 2020

DUBAI, UAE, TIME (GMT+4) complete session video

• Ali Ackouglu, Arizona University, USA
• Khalid Belhajjame, University Paris Dauphine, LAMSADE, France
• Cheyma Ben Njima, MARS Lab, Tunisia
• Dalila Chiadmi, Mohammadia School of Engineers, Morocco
• Umberto Costa, UFRN, Brazil
• Javier A. Espinosa Oviedo, University Jean Moulin Lyon 3, France
• Luciano García Bañuelos, Tecnológico de Monterrey, Mexico
• Rima Grati, Zayed University, EAU
• Carmem Hara, Universidade Federal do Parana
• Faiza Loukil, University Jean Moulin Lyon 3, France
• Riadh Mokadem, IRIT, France
• Frank Morvan, IRIT, France
• Michael Mrissa, InnoRenew COE, Slovenia
• Mourad Oussalah, University of Nantes, France
• Alex Palesandro, D2SI, France
• Placido Antonio Souza Neto, IFRN, Brazil –Sana Sellami, Université Aix Marseille, France
• Nicolas Travers, De Vinci Research Centre, Département Informatique, Big Data et Objets Connectés, France
• Sami Yangui, INSA de Toulouse, France
• José Luis Zechinelli Martini, Universidad de las Américas Puebla, México

Building edge and fog applications on the FogStore platform

Today’s applications in domains such as the Internet of Things are no longer cloud-based. Instead, they often rely on edge and fog resources to address latency and privacy requirements or to deal with bandwidth limitations. Handling the complexity of such environments, however, often exceeds the capabilities of the average software developer. In this talk, we will give an overview of the FogStore platform and the abstractions it provides which significantly eases the development of applications that go beyond cloud-only deployments.

David Bermbach is an Assistant Professor at TU Berlin and at the Einstein Center Digital Future in Berlin, Germany where he is heading the Mobile Cloud Computing research group. In his research, he is working on platforms and applications for cloud, edge, and fog computing, systems benchmarking, as well as interdisciplinary research topics. David has a Diploma in business engineering and a Ph.D. with distinction in computer science both from Karlsruhe Institute of Technology, Germany.

Enabling Interactivity between Human and Artificial Intelligence

Interactivity is to enable users to interact with their data and data systems effectively. It goes beyond efficiency measures and aims to make data usable for users. There exist different needs to interact with data and seek relevant information for an improved decision making process, such as shopping online, finding risky insured drivers, and building expert committees. In this talk, we discuss challenges and opportunities to substantiate interactivity in artificial intelligence applications. We begin by introducing the status quo, i.e., AI systems in which the user is left out of the loop, such as automated end-to-end ML pipelines, which lack personalization, customization, and explanation. Then we present a “guidance” approach in the form of a mixed-initiative system, where both human intelligence and artificial intelligence assist each other through iterative interactions to achieve higher levels of effectiveness. Next we describe how Reinforcement Learning helps model these interactions and how learned policies can augment the quality of interactions. Last, we discuss some results obtained so far and some future directions.

Behrooz Omidvar-Tehrani is a Research Scientist with a focus on interactive data systems. He has held positions in Naver Labs Europe, the Grenoble Alpes University, and the Ohio State University. His research interest is in the area of data management and at the crossroad of data mining, databases, and machine learning. He has published several papers in international conferences, such as VLDB, SIGMOD, and ICDE, and journals such as TKDE, VLDBJ, and FGCS.