Big and open linked data analytics ecosystem: Theoretical background and essential elements

Abstract

Big and open linked data are often mentioned together because storing, processing, and publishing large amounts of these data play an increasingly important role in today's society. However, although this topic is described from the political, economic, and social points of view, a technical dimension, which is represented by big data analytics, is insufficient. The aim of this review article was to provide a theoretical background of big and open linked data analytics ecosystem and its essential elements. First, the key terms were introduced including related dimensions. Then, the key lifecycle phases were defined and involved stakeholders were identified. Finally, a conceptual framework was proposed. In contrast to previous research, the new ecosystem is formed by interactions of stakeholders in the following dimensions and their sub-dimensions: transparency, engagement, legal, technical, social, and economic. These relationships are characterized by the most important requisites and public policy choices affecting the data analytics ecosystem together with the key phases and activities of the data analytics lifecycle. The findings should contribute to relevant initiatives, strategies, and policies and their effective implementation.

Application of an Ecosystem Methodology Based on Legal Language Processing for the Transformation of Court Decisions and Legal Opinions into Open Data

“…As Open Data ecosystems involve a data provider and a data user level [22], the researchers imagined that the above stages form two interdependent cycles [21]: the inner one referring to the data provider level (create, preprocess, curate, store/obtain, publish) and the outer one to the data user level (retrieve/acquire, process, use, collaborate with users, and provide feedback). Similarly, Lnenicka and Komarkova [24] focusing on Big and Open Linked Data (BOLD) ecosystems, identified several types of involved stakeholders (ecosystem orchestrator, service provider, application provider, data producer, data publisher, data user, and data prosumer) and proposed a BOLD analytics lifecycle consisting of six phases: acquisition and extraction, management and preparation, storage and archiving, processing and analysis, visualization and use, publication, sharing, Information 2020, 11, 10 4 of 30 and reuse. Each stakeholder participates in different phases of the model with a different role in each phase.…”

“…Our Methodology BOLD Analytics Lifecycle [24] Create/Gather The steps of our methodology for this Legal Open Data project are described below:…”

Platform and Data Co-Operatives amidst European Pandemic Citizenship

“…Data co-operatives that have fiduciary obligations to members demonstrate a promising direction for the democratic empowerment of citizens through their personal data. Without data co-operatives and related data policy ecosystems, the EU might lose its opportunity to establish a pan-European post-GDPR AI strategy [99,113,114]. As Aho and Duffield recently argued (p. 208), "Whilst China proceeds with constructive confidence, Europe lags behind, searching for a way to function in the global information civilization that is compatible with established Western political and social values" [67,99,115].…”

Integrated machine learning with semantic web for open government data recommendation based on cloud computing

“…Big data and OD are often discussed together because the heterogeneity, distribution, and processing of large amounts of such data play an important role in social platforms. 15 . Any user can post digital material to a social platform and thereby create large amounts of data.…”