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Although data lake technology has received increasing research attention in recent years with the popularity of Big Data and heterogeneous data technologies, it still receives little attention in the enterprise space and relation to relational databases. ...

In industry practice, zone models have been established as data lake architectures of choice to enable the reuse of data preparation, data modeling, and analytical results across the entire platform. However, when implementing a zone-based data ...

This study explores implementation of Data Lake for Big data analytics marked by intervention of quasi-outsourcing personnel. Though an agentic interventionist, it takes place in an active Multinational Consumer Goods company (MCGC) of socially ...

In the medical field, huge amounts of multi-modal medical data are generated daily from various smart devices. Besides EMRs, medical data include a large amount of unstructured data such as MRI scans, CT scans, and X-rays. These massive, ...

The management of modern solutions for Big Data management and analytics, most notably Data Lakes and Data Lakehouses, is faced with new challenges stemming from the versatility offered by such technologies, as well as the continuously evolving ...

Simulations and learning environments often generate massive amounts of human performance data, which can be a challenge to manage and interpret in a meaningful manner. Lacking a context that provides meaning to data, it exists as information ...

The ever-growing usage and popularity of Internet of Things devices, coupled with Big Data technologies and machine learning algorithms, have allowed for data engineers to explore new opportunities in healthcare and continuous care. Furthermore, ...

In a paradigm of value creation from data collection and analysis, using Big Data, and particularly mining techniques, industrial actors are confronted to a series of issue regarding data collection, storage, distribution, and management.

In this ...

The increased flexibility brought by Data Lake technologies, along with size and heterogeneity of quickly changing data sources, bring novel challenges to their management. Making sense of disparate data and supporting users to identify the most ...

Over the past decade, digital transformation has led to the evolution of databases towards Big Data. A need to collect and analyze data from different sources has emerged. At the same time, traditional decision support systems are unable to meet ...

In this paper a Big Data Pipeline is presented, taking in consideration both structured and unstructured data made available by the Italian Ministry of Justice, regarding their Telematic Civil Process. Indeed, the complexity and volume of the data ...

In the age of big data, the way we store and analyze heterogeneous data has changed. The complexity of various data inputs in the lakes indicates the significant importance of data ingestion that aids companies in making sense and getting more ...

Inherent to the growing use of the most varied forms of software (e.g., social applications), there is the creation and storage of data that, due to its characteristics (volume, variety, and velocity), make the concept of Big Data emerge. Big Data ...

Data Lake (DL) architectures have recently emerged as an effective solution to the problem of data analytics with big, highly heterogeneous, and quickly changing data sources. However, novel challenges arise too, including how to make sense of ...

Medical knowledge graph (MKG) provides ideal technical support for integrating multi-source heterogeneous data and enhancing graph-based services. These multi-source data are usually huge, heterogeneous, and difficult to manage. To ensure that the ...

In medical domain, huge amounts of data are generated at all times. These data are usually difficult to access, with poor data quality and many data islands. Besides, with a wide range of sources and complex structure, these data contain essential ...

With the rapid development of information technology, large amounts of multi-source data are constantly being generated in medical field. The automatic visualization system based on them has gained a lot of attention, since the intuitive data ...

Data lakes are supposed to enable analysts to perform more efficient and efficacious data analysis by crossing multiple existing data sources, processes and analyses. However, it is impossible to achieve that when a data lake does not have a metadata ...

Data lakes are supposed to enable analysts to perform more efficient and efficacious data analysis by crossing multiple existing data sources, processes and analyses. However, it is impossible to achieve that when a data lake does not have a metadata ...

The production of knowledge from ever increasing amount of social data is seen by many organizations as an increasingly important capability that can complement the traditional analytics sources. Examples include extracting knowledge and deriving ...