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.

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.

Purpose: NoSQL databases do not require a default schema associated with the data. Even that, they are categorized by data models. A model associated with the data can promote better strategies for persistence and manipulation of data in the target database. Based on this motivation, the purpose of this paper is to present an approach for logical design of NoSQL document databases that consists a process that converts a conceptual modeling into efficient logical representations for a NoSQL document database. The authors also evaluate their approach and demonstrate that the generated NoSQL logical structures reduce the amount of data items accessed by queries. Design/methodology/approach: This paper presents an approach for logical design of NoSQL document database schemas based on a conceptual schema. The authors generate compact and redundancy-free schemas and define appropriate representations in a NoSQL document logical model. The estimated volume of data and workload information can be considered to generate optimized NoSQL document structures. Findings: This approach was evaluated through a case study with an experimental evaluation in the e-commerce application domain. The results demonstrate that the authors’ workload-based conversion process improves query performance on NoSQL documents by reducing the number of database accesses. Originality/value: Unlike related work, the reported approach covers all typical conceptual constructs, details a conversion process between conceptual schemas and logical representations for NoSQL document database category and, additionally, considers the estimated database workload to perform optimizations in the logical structure. An experimental evaluation shows that the proposed approach is promising.

Governments, corporations, startups, open data initiatives and other organizations are increasingly considering RDF and SPARQL in a broad range of information management scenarios. To reduce SPARQL querying times has been the main issue for virtually all the recent RDF triplestores, yet SPARQL caching techniques have not been broadly considered. In this paper we present Rendezvous, a middleware that addresses workload-adaptive management of large RDF graphs with a caching strategy for SPARQL query results. Our middleware provides a novel RDF data partitioning approach based on a fragmentation strategy that maps RDF data into multiple NoSQL databases. The focus of this paper is also on Rendezvous caching, which can reduce average response time by up to an order of magnitude. Our experimental evaluation shows that the approach is promising, outperforming a recent key/value-based caching baseline.