This position paper identifies challenges that can be addressed through data-driven analytics to design inclusive urban spaces that meet women’s security and well-being expectations. To this end, it proposes a decision-making support platform that enables urban planners to identify quantitative and qualitative variables that influence quality of life (QoL) and understand how much each variable contributes to perceptions of well-being.

This paper provides a multi-perspective vision of diversity and inclusion (D&I) projects aiming to promote equity in organisations seeking to build virtuous contexts where people can achieve positive professional and personal objectives. It introduces the understanding of D&I, best practices and outcomes of projects promoted in multicultural organisations, including academia, universities and research centres (Politecnico di Torino, university education in France and the French CNRS) and in leading international companies, namely Accenture and Nestlé. The paper gathers and extends the discussion and ideas exchanged in the D&I panel of the conference ADBIS-2022.

Today, large amounts of data are collected in various domains, presenting unprecedented economic and societal opportunities. Yet, at present, the exploitation of these data sets through data science methods is primarily dominated by AI-savvy users. From an inclusive perspective, there is a need for solutions that can democratise data science that can guide non-specialists intuitively to explore data collections and extract knowledge out of them. This paper introduces the vision of a new data science engine, called DS4ALL (Data Science for ALL), that empowers users who are neither computer nor AI experts to perform sophisticated data exploration and analysis tasks. Therefore, DS4ALL is based on a conversational and intuitive approach that insulates users from the complexity of AI algorithms. DS4ALL allows a dialogue-based approach that gives the user greater freedom of expression. It will enable them to communicate using natural language without requiring a high level of expertise on data-driven algorithms. User requests are interpreted and handled internally by the system in an automated manner, providing the user with the required output by masking the complexity of the data science workflow. The system can also collect feedback on the displayed results, leveraging these comments to address personalized data analysis sessions. The benefits of the envisioned system are discussed, and a use case is also presented to describe the innovative aspects.

International audience

The emergence of new hardware architectures, and the continuous production of data open new challenges for data management. It is no longer pertinent to reason with respect to a predefined set of resources (i.e., computing, storage and main memory). Instead, it is necessary to design data processing algorithms and processes considering unlimited resources via the “pay-as-you-go” model. According to this model, resources provision must consider the economic cost of the processes versus the use and parallel exploitation of available computing resources. In consequence, new methodologies, algorithms and tools for querying, deploying and programming data management functions have to be provided in scalable and elastic architectures that can cope with the characteristics of Big Data aware systems (intelligent systems, decision making, virtual environments, smart cities, drug personalization). These functions, must respect QoS properties (e.g., security, reliability, fault tolerance, dynamic evolution and adaptability) and behavior properties (e.g., transactional execution) according to application requirements. Mature and novel system architectures propose models and mechanisms for adding these properties to new efficient data management and processing functions delivered as services. This paper gives an overview of the different architectures in which efficient data management functions can be delivered for addressing Big Data processing challenges.

The emergence of mobile and ambient computing technologies brings democratization in the access to information and data; services play a crucial role, thereby opening new research challenges for data querying. A promising method to access data within these novel dynamic environments in a convenient and efficient way is declarative querying. Such queries, which we characterize as hybrid queries, involve streaming and on-demand data originated from services, possibly with temporal and mobile properties. To evaluate these queries we propose an approach implemented in the HYPATIA system, which addresses two main aspects (i) using service coordination for query evaluation and (ii) an efficient and flexible mechanism that facilitates incorporating new capabilities.

This paper presents an approach for building reliable applications based on services oriented paradigm and the use of a contract based coordination model. Thanks to contracts describing the logic of a services based application running in a dynamic environment it is possible to associate a personalized behavior in an orthogonal way. Contracts ensure, for example, transactional properties at execution time in the presence of exceptions and make applications aware of their execution context.

This paper proposes a flexible transactional behavior model for services coordination. We assume that given a flow describing the application logic of a service based application, it is possible to associate to it a personalised transactional behaviour in an orthogonal way. This behaviour is defined by specifying contracts and associating a well defined behaviour to the activities participating in the coordination. Such contracts ensure transactional properties at execution time in the presence of exceptions

Service oriented computing has gained a considerable momentum as a new paradigm for building enterprise information systems. Notable efforts have been made recently from both researchers and industrials to support the construction of service-based applications, nevertheless several issues still need to be tackled including service definition and adaptation, and services orchestration. This work proposes an approach for building and finely orchestrating open and adaptable services. An open service is represented by a workflow that coordinates calls to service provider methods. Thereby component activities and the way they are synchronized are rendered visible. Service adaptability refers to the possibility to modify an open service. Through adaptation operations a service can be customized according to given user (application) requirements. In order to finely orchestrate services, they are associated with entry points. An entry point acts as a gateway for inserting and getting information about the progress of service execution. Defined services and orchestration are verified to ensure a correct behaviour of the resulting application. The paper details our approach for building and orchestrating services, and presents associated architectural choices.

This paper presents TERRA and ACQUA, two mechanisms that support adaptable definition and execution of workflows. TERRA generates and stores structures (objects, events, reactions) necessary for executing a workflow. AQUA executes workflows according to a parametric behaviour model that represents workflow execution policies in terms of dimensions associated to values. TERRA and ACQUA provide mechanisms for modifying the structure of a workflow and its associated behaviour.

This paper introduces definition and enactment models to characterize workflows. The proposed knowledge model provides concepts for modeling activities, data used by such activities, agents, and ordering operators and synchronization modes used for specifying activities dependencies. The behavior model provides dimensions that characterize workflow enactment. Both models are currently implemented in our workflow management system called AFLOWS. The originality of our approach is related to its flexibility as different enactment models can be associated to a workflow simply by giving values to dimensions according to application needs. Such an approach and system provide a suitable support for specifying and enacting workflow processes that may be useful today to describe and monitor process-oriented applications (i.e., large Web-based applications that run across wide area networks and multiple enterprises).

Applications are now highly distributed, heterogeneous and scalable. They comprise autonomous, loosely coupled components, some of them being database management systems, Web servers, etc. Considering such a framework, database technology has to evolve towards cooperation and integration. We propose open and distributed active services that can be used for specifying and generating event and rule managers that support cooperation and interaction between distributed database software (i.e. applications and systems). Event managers are brokers supporting anonymous event-passing communications. Rule managers are able to support global (business) rules and execute them using different policies that are adapted to specific application needs. Both managers can reconfigure and adapt themselves with respect to application requirements and to their environment.