Towards Semantification of Big Data Technology

Mami, Mohamed Nadjib; Scerri, Simon; Auer, Sören; Vidal, María-Esther

doi:10.1007/978-3-319-43946-4_25

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…The semantic approach to data governance has risen to solve the problems associated with the management of great volume and their variety. There are several references about the "Semantification" of big data Technology, like those introduced in [24], [25], [26]. Furthermore, an Ontology-Based Data Management (OBDM) was created to access and use data by means of ontologies [27].…”

Section: Related Workmentioning

confidence: 99%

An Ontological-Based Model to Data Governance for Big Data

et al. 2021

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Nowadays, companies and official bodies are using the data as a principal asset to take strategic decision. The advances in big data processing, storage and analysis techniques have allowed to manage the continuous increase in the volume of data. This increase in the volume of data together with its high variability and the large number of sources lead to a constant growing of the complexity of the data management environment. Data governance is the key for simplifying that complexity: it is the element that controls the decision making and responsibilities for all the processes related to data management. This paper discusses an approach to data governance based on ontological reasoning to reduce data management complexity. The proposed data governance system is built over an autonomous system based on distributed components. It uses semantic techniques and automatic ontology-based reasoning, with the different component using a Shared Knowledge Plane to interact. Its fundamental piece is an ontology that represents all the data management processes included in data governance. A prototype of such a system has been implemented and tested for Telefonica´s global video service. The results obtained show the feasibility of using this type of technology to reduce the complexity of managing big data environments.INDEX TERMS Big data, data governance, knowledge-based management, ontologies, reasoning, OWL, SWRL.

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Section: Related Workmentioning

confidence: 99%

An Ontological-Based Model to Data Governance for Big Data

et al. 2021

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“…text [23], RDBs [24], and XML [25]). Current research is mostly focused on NoSQL document stores [20,[26][27][28][29][30]. In [29], a process of giving big data a virtual layer of high-level metadata description is called the semantification of big data.…”

Section: Semantification Of Data Sourcesmentioning

confidence: 99%

“…Current research is mostly focused on NoSQL document stores [20,[26][27][28][29][30]. In [29], a process of giving big data a virtual layer of high-level metadata description is called the semantification of big data. However, schema heterogeneity problems are also present during ontology derivation, making the fully manual approach unsuitable.…”

Section: Semantification Of Data Sourcesmentioning

confidence: 99%

The potential of semantic paradigm in warehousing of big data

2019

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Big data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases.

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“…Due to growing data collections, diversity of data sources and an increasing number of use cases, any automation of geoinformation processing is needed. It follows aspects of data integration, data interpretation (Sester 2010), data "semantification" (Mami et al 2016) and data transmission preparation. For the variety of components, that provide functionality, Service-Oriented Architecture (SOA) is the main paradigm to connect and explore everything (Hendriks 2012).…”

Section: Geoinformation Processingmentioning

confidence: 99%

Map Production Supporting Activities of Digitalization and AI

Jobst

Gärtner

2019

Proc. Int. Cartogr. Assoc.

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<p><strong>Abstract.</strong> The notion “digitalization” comes into vogue in many corners of information and knowledge technologies. Even politicians are aware of the power of digitalization and its impact on society. In general digitalization is not directly dealing with webmapping or location based services, although digital and web-based procedures are the key. But what else could these initiatives of digitalization mean for the mapping domain? Is there an impact on map production, geoinformation management and the way we provide and use geospatial knowledge? The aim of this contribution is to formally extend the function of cartography by the aspect of artificial knowledge expansion and therefore to highlight the basics, requirements and additional emerging methods in map production.</p>

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Towards Semantification of Big Data Technology

Cited by 15 publications

References 9 publications

An Ontological-Based Model to Data Governance for Big Data

An Ontological-Based Model to Data Governance for Big Data

The potential of semantic paradigm in warehousing of big data

Map Production Supporting Activities of Digitalization and AI

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