Related papers: Modeling Data Lake Metadata with a Data Vault

One of the purposes of Big Data systems is to support analysis of data gathered from heterogeneous data sources. Since data warehouses have been used for several decades to achieve the same goal, they could be leveraged also to provide…

The web of data has brought forth the need to preserve and sustain evolving information within linked datasets; however, a basic requirement of data preservation is the maintenance of the datasets' structural characteristics as well. As…

Data warehouse store and provide access to large volume of historical data supporting the strategic decisions of organisations. Data warehouse is based on a multidimensional model which allow to express user's needs for supporting the…

A data lake is a repository of data with potential for future analysis. However, both discovering what data is in a data lake and exploring related data sets can take significant effort, as a data lake can contain an intimidating amount of…

Enterprises often own large collections of structured data in the form of large databases or an enterprise data lake. Such data collections come with limited metadata and strict access policies that could limit access to the data contents…

Metadata represents the information about data to be stored in Data Warehouses.It is a mandatory element of Data Warehouse to build an efficient Data Warehouse.Metadata helps in data integration,lineage,data quality and populating…

The Big Data landscape poses challenges in managing diverse data formats, requiring efficient storage and processing for high-quality analysis. Effective metadata management is crucial for organizing, accessing, and reusing data within…

Cloud data lakes provide a modern solution for managing large volumes of data. The fundamental principle behind these systems is the separation of compute and storage layers. In this architecture, inexpensive cloud storage is utilized for…

With the ever-growing availability of so-called complex data, especially on the Web, decision-support systems such as data warehouses must store and process data that are not only numerical or symbolic. Warehousing and analyzing such data…

Data analytics stands to benefit from the increasing availability of datasets that are held without their conceptual relationships being explicitly known. When collected, these datasets form a data lake from which, by processes like data…

In the last few years, the concept of data lake has become trendy for data storage and analysis. Thus, several design alternatives have been proposed to build data lake systems. However, these proposals are difficult to evaluate as there…

Relational Database Management Systems designed for Online Analytical Processing (RDBMS-OLAP) have been foundational to democratizing data and enabling analytical use cases such as business intelligence and reporting for many years.…

Since the use of computers in the business world, data collection has become one of the most important issues due to the available knowledge in the data; such data has been stored in the database. The database system was developed which led…

In a data warehousing process, the data preparation phase is crucial. Mastering this phase allows substantial gains in terms of time and performance when performing a multidimensional analysis or using data mining algorithms. Furthermore, a…

Large, open datasets can accelerate ecological research, particularly by enabling researchers to develop new insights by reusing datasets from multiple sources. However, to find the most suitable datasets to combine and integrate,…

The digital transformation of the energy infrastructure enables new, data driven, applications often supported by machine learning models. However, domain specific data transformations, pre-processing and management in modern data driven…

Data Lake (DL) is a Big Data analysis solution which ingests raw data in their native format and allows users to process these data upon usage. Data ingestion is not a simple copy and paste of data, it is a complicated and important phase…

Advances in technology and computing hardware are enabling scientists from all areas of science to produce massive amounts of data using large-scale simulations or observational facilities. In this era of data deluge, effective coordination…

Data warehouses are overwhelmingly built through a bottom-up process, which starts with the identification of sources, continues with the extraction and transformation of data from these sources, and then loads the data into a set of data…

Research in data warehousing and OLAP has produced important technologies for the design, management and use of information systems for decision support. With the development of Internet, the availability of various types of data has…