For many years trajectory data have been treated as sequences of space‐time points or stops and moves. However, with the explosion of the Internet of Things and the flood of big data generated on the Internet, such as weather channels and social network interactions, which can be used to enrich mobility data, trajectories become more and more complex, with multiple and heterogeneous data dimensions. The main challenge is how to integrate all this information with trajectories. In this article we introduce a new concept of trajectory, called multiple aspect trajectory, propose a robust conceptual and logical data model that supports a vast range of applications, and, differently from state‐of‐the‐art methods, we propose a storage solution for efficient multiple aspect trajectory queries. The main strength of our data model is the combination of simplicity and expressive power to represent heterogeneous aspects, ranging from simple labels to complex objects. We evaluate the proposed model in a tourism scenario and compare its query performance against the state‐of‐the‐art spatio‐temporal database SECONDO extension for symbolic trajectories.

Today, many applications produce and manipulate a large volume of data, the so-called Big Data. Traditional databases (DB), like relational databases, are not suitable to Big Data management. In order to solve this problem, a new category of DB has been proposed, the so-called NoSQL DB. NoSQL DB have different data models, as well as different access methods which are not usually compatible with the SQL language. In this context, approaches have been proposed for providing mapping of relational DB schemata and operations to equivalent ones in NoSQL DB to deal with large relational data sets in the cloud, focusing on scalability and availability. However, these approaches map relational DB only to a single NoSQL data model and, sometimes, to a specific NoSQL DB product. This paper presents SQLtoKeyNoSQL, a layer able to translate relational schemata as well as SQL commands to equivalent schemata and access methods to any key-oriented NoSQL DB (document-oriented, key-value and column-oriented). We present the architecture of our layer focusing on our mapping strategies, as well as some preliminary experiments that evaluate the impact of SQLtoKeyNoSQL as a solution for transparent mapping of relational DB to key-based NoSQL DB.

Several data-centric applications today produce and manipulate a large volume of data, the so-called Big Data. Traditional databases, in particular, relational databases, are not suitable for Big Data management. As a consequence, some approaches that allow the definition and manipulation of large relational data sets stored in NoSQL databases through an SQL interface have been proposed, focusing on scalability and availability. This paper presents a comparative analysis of these approaches based on an architectural classification that organizes them according to their system architectures. Our motivation is that wrapping is a relevant strategy for relational-based applications that intend to move relational data to NoSQL databases (usually maintained in the cloud). We also claim that this research area has some open issues, given that most approaches deal with only a subset of SQL operations or give support to specific target NoSQL databases. Our intention with this survey is, therefore, to contribute to the state-of-art in this research area and also provide a basis for choosing or even designing a relational-to-NoSQL data wrapping solution.

Several applications require to handle large and heterogeneous data volumes as well as thousands of OLTP transactions per second. Traditional relational databases are not suitable for these requirements. On the other hand, NoSQL databases are able to deal with Big Data, but lacks the support to the traditional ACID properties. NewSQL is a new class of databases that combines the support to OLTP transactions of relational databases with the high availability and scalability of NoSQL databases. However, few works in the literature explore the differences among different NewSQL solutions. In this paper, we discuss the main features of the most prominent NewSQL products, besides we present benchmarking results for analyzing their performance. We believe that both analysis can be useful as a guide to a future choice of NewSQL technologies.