Digital revolution produces massive, heterogeneous and isolated data. These latter remain underutilized, unsuitable for integrated querying and knowledge discovering. Hence the importance of this survey on data integration which identifies challenging ...

The development of the Internet of Things (IoT) paradigm and its significant spread as an affordable data source has brought many challenges when pursuing efficient data collection, distribution, and storage. Since such hierarchical logical architecture ...

Knowledge graph (KG) refinement refers to the process of filling in missing information, removing redundancies, and resolving inconsistencies in KGs. With the growing popularity of KG in various domains, many techniques involving machine learning have ...

Data scientists are tasked with obtaining insights from data. However, suitable data is often not immediately at hand, and there may be many potentially relevant datasets in a data lake or in open data repositories. As a result, data discovery and ...

The Internet of Things (IoT) is enabled by the latest developments in smart sensors, communication technologies, and Internet protocols with broad applications. Collecting data from IoT and generating information from these data become tedious tasks in ...

In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After ...

One of the most critical tasks for improving data quality and increasing the reliability of data analytics is Entity Resolution (ER), which aims to identify different descriptions that refer to the same real-world entity. Despite several decades of ...

Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream or hybrid fashion), and served to end-users and external ...

Entity Resolution (ER), a core task of Data Integration, detects different entity profiles that correspond to the same real-world object. Due to its inherently quadratic complexity, a series of techniques accelerate it so that it scales to voluminous ...

A large number of published datasets (or sources) that follow Linked Data principles is currently available and this number grows rapidly. However, the major target of Linked Data, i.e., linking and integration, is not easy to achieve. In general, ...

The variety of data is one of the most challenging issues for the research and practice in data management systems. The data are naturally organized in different formats and models, including structured data, semi-structured data, and unstructured data. ...

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ...

Recently, we have witnessed entity profiling (EP) becoming increasingly one of the most important topics in information extraction, personalized applications, and web data analysis. EP aims to identify, extract, and represent a compact summary of ...

Improving disaster management and recovery techniques is one of national priorities given the huge toll caused by man-made and nature calamities. Data-driven disaster management aims at applying advanced data collection and analysis technologies to ...

Data deduplication has attracted many cloud service providers (CSPs) as a way to reduce storage costs. Even though the general deduplication approach has been increasingly accepted, it comes with many security and privacy problems due to the outsourced ...

In the last two decades, the continuous increase of computational power has produced an overwhelming flow of data which has called for a paradigm shift in the computing architecture and large-scale data processing mechanisms. MapReduce is a simple and ...

The development of the Internet in recent years has made it possible and useful to access many different information systems anywhere in the world to obtain information. While there is much research on the integration of heterogeneous information ...

Graph database models can be defined as those in which data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models ...