Since the boom in new proposals on techniques for efficient querying of XML data is now over and the research world has shifted its attention toward new types of data formats, we believe that it is crucial to review what has been done in the area to help users choose an appropriate strategy and scientists exploit the contributions in new areas of data processing. The aim of this work is to provide a comprehensive study of the state-of-the-art of approaches for the structural querying of XML data. In particular, we start with a description of labeling schemas to capture the structure of the data and the respective storage strategies. Then we deal with the key part of every XML query processing: a twig query join, XML query algebras, optimizations of query plans, and selectivity estimation of XML queries. To the best of our knowledge, this is the first work that provides such a detailed description of XML query processing techniques that are related to structural aspects and that contains information about their theoretical and practical features as well as about their mutual compatibility and general usability.

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. In this survey, we introduce the area of multi-model DBMSs that build a single database platform to manage multi-model data. Even though multi-model databases are a newly emerging area, in recent years, we have witnessed many database systems to embrace this category. We provide a general classification and multi-dimensional comparisons for the most popular multi-model databases. This comprehensive introduction on existing approaches and open problems, from the technique and application perspective, make this survey useful for motivating new multi-model database approaches, as well as serving as a technical reference for developing multi-model database applications.

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The abundance of interconnected data has fueled the design and implementation of graph generators reproducing real-world linking properties or gauging the effectiveness of graph algorithms, techniques, and applications manipulating these data. We consider graph generation across multiple subfields, such as Semantic Web, graph databases, social networks, and community detection, along with general graphs. Despite the disparate requirements of modern graph generators throughout these communities, we analyze them under a common umbrella, reaching out the functionalities, the practical usage, and their supported operations. We argue that this classification is serving the need of providing scientists, researchers, and practitioners with the right data generator at hand for their work. This survey provides a comprehensive overview of the state-of-the-art graph generators by focusing on those that are pertinent and suitable for several data-intensive tasks. Finally, we discuss open challenges and missing requirements of current graph generators along with their future extensions to new emerging fields.

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