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Data science is not a science. It is a research paradigm with an unfathomed scope, scale, complexity, and power for knowledge discovery that is not otherwise possible and can be beyond human reasoning. It is changing our world practically and profoundly already widely deployed in tens of thousands of applications in every discipline in an AI Arms Race that, due to its inscrutability, can lead to unfathomed risks. This paper presents an axiology of data science, its purpose, nature, importance, risks, and value for problem solving, by exploring and evaluating its remarkable, definitive features. As data science is in its infancy, this initial, speculative axiology is intended to aid in understanding and defining data science to recognize its potential benefits, risks, and open research challenges. AI based data science is inherently about uncertainty that may be more realistic than our preference for the certainty of science. Data science will have impacts far beyond knowledge discovery and will take us into new ways of understanding the world.

Data science is not a science. It is a research paradigm. Its power, scope, and scale will surpass science, our most powerful research paradigm, to enable knowledge discovery and change our world. We have yet to understand and define it, vital to realizing its potential and managing its risks. Modern data science is in its infancy. Emerging slowly since 1962 and rapidly since 2000, it is a fundamentally new field of inquiry, one of the most active, powerful, and rapidly evolving 21st century innovations. Due to its value, power, and applicability, it is emerging in over 40 disciplines, hundreds of research areas, and thousands of applications. Millions of data science publications contain myriad definitions of data science and data science problem solving. Due to its infancy, many definitions are independent, application specific, mutually incomplete, redundant, or inconsistent, hence so is data science. This research addresses this data science multiple definitions challenge by proposing the development of coherent, unified definition based on a data science reference framework using a data science journal for the data science community to achieve such a definition. This paper provides candidate definitions for essential data science artifacts that are required to discuss such a definition. They are based on the classical research paradigm concept consisting of a philosophy of data science, the data science problem solving paradigm, and the six component data science reference framework (axiology, ontology, epistemology, methodology, methods, technology) that is a frequently called for unifying framework with which to define, unify, and evolve data science. It presents challenges for defining data science, solution approaches, i.e., means for defining data science, and their requirements and benefits as the basis of a comprehensive solution.

© 2019, Springer Nature Switzerland AG. GDPR in Europe and similar regulations, such as the California CCPA, require new levels of privacy support for consumers. Most challenging to IT departments is the “right to be forgotten”. Hence, an enterprise must ensure that ALL information about a specific consumer be deleted from enterprise storage, when requested. Since enterprises are internally heavily “siloed”, sharing of information is usually accomplished by copying data between systems. This makes finding and deleting all copies of data on a particular consumer difficult. GDPR also requires the notion of purposes, which is an access control model orthogonal to the one customarily in SQL. Herein, we sketch an implementation of purposes and show how it fits within a conventional access control framework. We then propose two solutions to supporting GDPR in a DBMS. When a “green field” environment is present, we propose a solution which directly supports the process of ensuring GDPR compliance at enterprise-scale. Specifically, it is designed to store every fact about a consumer exactly once. Therefore, the right to be forgotten is readily supported by deleting that fact. On the other hand, when dealing with legacy systems in the enterprise, we propose a second solution which tracks all copies of personal information, so they can be deleted on request. Of course, this solution entails additional overhead in the DBMS. Once data leaves the DBMS, it is in some application. We propose “sandboxing” applications in a novel way that will prevent them from leaking data to the outside world when inappropriate. Lastly, we discuss the challenges associated with auditing and logging of data. This paper sketches the design of the above GDPR compliant facilities, which we collectively term SchengenDB.

After four decades of rapid advances in computing, we are embarking on the greatest leap forward in computing that includes revolutionary changes at all levels of computing from the hardware through the middleware and infrastructure to applications and more importantly in intelligence. This paper outlines a comprehensive framework that integrates two complimentary and revolutionary technical advances, ServiceOriented Architectures (SOA) and Semantic Web, into a single computing architecture, that we call Semantically Enabled Service Oriented Architecture (SESA). While SOA is widely acknowledged for its potential to revolutionize the world of computing, that success depends on resolving two fundamental challenges that SOA does not address, integration, and search or mediation. In a servicesoriented world, billions of services must be discovered and selected based on requirements, then orchestrated and adapted or integrated. SOA depends on but does not address either search or integration. The contribution of this paper is to provide the semanticsbased solution to search and integration that will enable the SOA revolution. The paper provides a vision of the future enabled by our framework that places computing and programming at the services layer and places the real goal of computing, problem solving, in the hands of end users.