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As data management applications grow more complex, they may need efficient distributed query processing, but also subscription management, data archival etc. To enact such applications, the current solution consists of stacking several systems together. The juxtaposition of different computing models prevents reasoning on the application as a whole, and wastes important opportunities to improve performance. We present a simple extension to the AXML language, allowing it to declaratively specify and deploy complex applications based solely on XML and XML queries. Our main contribution is a full algebraic model for complex distributed AXML computations. While very expressive, the model is conceptually uniform, and enables numerous powerful optimizations across a distributed complex process. This work was partially supported by the French Government ACI MDP2P and the eDos EU project.

As data management applications grow more complex, they may need efficient distributed query processing, but also subscription management, data archival etc. To enact such applications, the current solution consists of stacking several systems together. The juxtaposition of different computing models prevents reasoning on the application as a whole, and wastes important opportunities to improve performance. We present a simple extension to the AXML language, allowing it to declaratively specify and deploy complex applications based solely on XML and XML queries. Our main contribution is a full algebraic model for complex distributed AXML computations. While very expressive, the model is conceptually uniform, and enables numerous powerful optimizations across a distributed complex process. This work was partially supported by the French Government ACI MDP2P and the eDos EU project.

As data management applications grow more complex, they may need efficient distributed query processing, but also subscription management, data archival etc. To enact such applications, the current solution consists of stacking several systems together. The juxtaposition of different computing models prevents reasoning on the application as a whole, and wastes important opportunities to improve performance. We present a simple extension to the AXML language, allowing it to declaratively specify and deploy complex applications based solely on XML and XML queries. Our main contribution is a full algebraic model for complex distributed AXML computations. While very expressive, the model is conceptually uniform, and enables numerous powerful optimizations across a distributed complex process. This work was partially supported by the French Government ACI MDP2P and the eDos EU project.

Combining data into a centralized, searchable, and linked platform will provide a data exploration platform to agricultural stakeholders and researchers for better agricultural decision making, thus fully utilizing existing data and preventing redundant research. Such a data repository requires readiness to share data, knowledge, and skillsets and working with Big Data infrastructures. With the adoption of new technologies and increased data collection, agricultural workforces need to update their knowledge, skills, and abilities. The partnerships for data innovation (PDI) effort integrates agricultural data by efficiently capturing them from field, lab, and greenhouse studies using a variety of sensors, tools, and apps and provides a quick visualization and summary of statistics for real-time decision making. This paper aims to evaluate and provide examples of case studies currently using PDI and use its long-term continental US database (18 locations and 24 years) to test the cover crop and grazing effects on soil organic carbon (SOC) storage. The results show that legume and rye (Secale cereale L.) cover crops increased SOC storage by 36% and 50%, respectively, compared with oat (Avena sativa L.) and rye mixtures and low and high grazing intensities improving the upper SOC by 69–72% compared with a medium grazing intensity. This was likely due to legumes providing a more favorable substrate for SOC formation and high grazing intensity systems having continuous manure deposition. Overall, PDI can be used to democratize data regionally and nationally and therefore can address large-scale research questions aimed at addressing agricultural grand challenges.

The flood of digital data, which arises from studies in the social sciences or results from satellite missions in earth or space sciences, is growing rapidly. The permanent storage and its provision for future generations of researchers represent a challenge to the entire science system, however, many questions still remain unresolved. Financial aspects, organizational and technology issues in creating multi-disciplinary research data infrastructures, as well as legal and political framework need to be clarified. These challenges will be discussed in the context of this session, which will take the form of a discussion panel introduced by four presentations The data life cycle will serve as the guideline for the presentations which take a closer look at its specific challenges. The overall objective is the development of a multi-disciplinary research data infrastructure. The second presentation takes a closer look at the Group Domain represented by VREs.

This chapter examines the roles of empirical research in consumer law. It does so form a contemporary perspective, taking into account the increased recourse to empirical data both in legal scholarship and in policy-making in conjunction with the behavioural turn. It addresses primarily legal researchers educated in a doctrinal tradition who take an interest in consumer law and are considering adding an empirical dimension to their work or want to incorporate empirical work among the sources they use. In this perspective, part 1 offers a typology of legal questions and discusses how each relates to empirical issues. A distinction is drawn between internal legal questions (questions about how rules relate to one another) and external legal questions (questions about law and the world). It is shown that, while external legal questions about effectiveness and efficiency of rules present a natural affinity with empirics, internal legal questions can also have an empirical component. Taking examples in consumer law, the chapter illustrates that empirical issues lie in the midst of questions about validity, proportionality of interpretation of rules. Section 1 also highlights the particular interest of one type of external legal question for empirical research, besides issues of effectiveness and efficiency, namely reality-check questions, which confront implicit behavioural claims embedded in the law with what is known of consumer behaviour (or indeed firms' behaviour). Part 2 reviews an illustrative selection of recent empirical work, which, strikingly, all pertain to external legal questions. It characterises the use of data in legal argument as rhetorical and illustrates this claim with two series of examples. Legal discourse based on data with express a critique of existing legal regimes provide the most striking illustrations. The rhetorical intensity recedes in reflexion about policy inception and fine-tuning of policy-interventions. Yet, all the examples show that lawyers only ever use data to make arguments. Turning to enforcement of consumer law, a recent trend in the literature advocates more data-intensive enforcement methods. Such proposals, along with substantive and procedural questions raised by algorithmic powered commercial practices provide rich perspectives for further research outlined in section 3.

This chapter examines the roles of empirical research in consumer law. It does so form a contemporary perspective, taking into account the increased recourse to empirical data both in legal scholarship and in policy-making in conjunction with the behavioural turn. It addresses primarily legal researchers educated in a doctrinal tradition who take an interest in consumer law and are considering adding an empirical dimension to their work or want to incorporate empirical work among the sources they use. In this perspective, part 1 offers a typology of legal questions and discusses how each relates to empirical issues. A distinction is drawn between internal legal questions (questions about how rules relate to one another) and external legal questions (questions about law and the world). It is shown that, while external legal questions about effectiveness and efficiency of rules present a natural affinity with empirics, internal legal questions can also have an empirical component. Taking examples in consumer law, the chapter illustrates that empirical issues lie in the midst of questions about validity, proportionality of interpretation of rules. Section 1 also highlights the particular interest of one type of external legal question for empirical research, besides issues of effectiveness and efficiency, namely reality-check questions, which confront implicit behavioural claims embedded in the law with what is known of consumer behaviour (or indeed firms' behaviour). Part 2 reviews an illustrative selection of recent empirical work, which, strikingly, all pertain to external legal questions. It characterises the use of data in legal argument as rhetorical and illustrates this claim with two series of examples. Legal discourse based on data with express a critique of existing legal regimes provide the most striking illustrations. The rhetorical intensity recedes in reflexion about policy inception and fine-tuning of policy-interventions. Yet, all the examples show that lawyers only ever use data to make arguments. Turning to enforcement of consumer law, a recent trend in the literature advocates more data-intensive enforcement methods. Such proposals, along with substantive and procedural questions raised by algorithmic powered commercial practices provide rich perspectives for further research outlined in section 3.

Warehouse management has emerged as a determinant for success of global supply chain management. This thesis focuses on how to solve warehouse challenges in global supply chain management (SCM) that is characterized by large volume uncertainty, great responsiveness needs and complex order-fulfilment collaboration with other functionalities. We employ data analytic methods to exploit the rich data information obtained from detailed registration of daily warehouse operations to address these challenges. By providing actual application examples in real-world situations we showcase the potency of such data-driven warehouse management. In this dissertation, data-driven warehouse management is presented by four-steps in the time horizon of warehouse operations: Long-term opportunities (for the coming years) are examined by predictive analytics for expanding cross-border e-commerce in the European Union. Mid-term demand for spare parts during the end-of-life phase (of several months) are forecasted by means of data-driven modelling for installed base. Short-term operational opportunity (weekly or daily) are presented by employing detailed productivity data to sustain effective operation of variable warehouse resources. Real-time (hourly or shorter) data applications are introduced for job priority allocation to improve daily responsiveness in warehouse order fulfilment. All these data analytic methods can be incorporated in warehouse management systems where practitioners can tune the specific strategies according to their warehouse constraints, including location cost, labour cost, time criticality, and freight company flexibility. In this way, data analytics at the warehouse level offers great opportunities for managing increasing uncertainties and performance requirements in global SCM.

A multitude of legislation impacts the use of samples and data for research in South Africa (SA). With the coming into effect of the Protection of Personal Information Act No. 4 of 2013 (POPIA) in July 2021, recent attention has been given to safeguarding research participants' personal information. The protection of participants' privacy in research is essential, but it is not the only risk at stake in the use and sharing of personal information. Other rights and interests that must also be considered and safeguarded include the right to non-discrimination, the right to dignity, and the right to enjoy the benefits of scientific progress. This can be achieved by ensuring that the ethical values and universal principles that underpin the research landscape, namely equity, reciprocity, justice and solidarity, are embedded in the regulatory framework for the management and use of personal information. This paper advocates an integrated bioethics approach to the use of personal information for research in SA. A key feature in embedding such an approach is a Data Transfer Agreement (DTA) that appropriately integrates the safeguards and protections set out by our regulatory framework with bioethical rules and procedures when personal information is used and shared for research purposes. This paper thus calls for the development of a national DTA that is guided by key ethical principles when data are shared for research purposes. This paper will be followed by a webinar on 23 June 2022 hosted by the Department of Science and Innovation and the SA Medical Research Council, where the recommendations and suggestions set out herein will be unpacked, discussed and debated by relevant stakeholders.

The phenomenon of big data does not only deeply affect current societies but also poses crucial challenges to social research. This article argues for moving towards a sociology of social research in order to characterize the new qualities of big data and its deficiencies. We draw on the neopragmatist approach of economics of convention (EC) as a conceptual basis for such a sociological perspective. This framework suggests investigating processes of quantification in their interplay with orders of justifications and logics of evaluation. Methodological issues such as the question of the "quality of big data" must accordingly be discussed in their deep entanglement with epistemic values, institutional forms, and historical contexts and as necessarily implying political issues such as who controls and has access to data infrastructures. On this conceptual basis, the article uses the example of health to discuss the challenges of big data analysis for social research. Phenomena such as the rise of new and massive privately owned data infrastructures, the economic valuation of huge amounts of connected data, or the movement of "quantified self" are presented as indications of a profound transformation compared to established forms of doing social research. Methodological and epistemological, but also institutional and political, strategies are presented to face the risk of being "outperformed" and "replaced" by big data analysis as they are already done in big US American and Chinese Internet enterprises. In conclusion, we argue that the sketched developments have important implications both for research practices and methods teaching in the era of big data.