Generating synthetic data similar to realistic data is a crucial task in data augmentation and data production. Due to the preservation of authentic data distribution, synthetic data provide concealment of sensitive information and therefore enable Big Data acquisition for model training without facing privacy challenges. Nevertheless, the obstacles arise starting with acquiring real-world open-source data to effectively synthesizing new samples as genuine as possible. In this paper, a comparative study is conducted by considering the efficacy of different generative models like Generative Adversarial Networks (GAN), Variational Autoencoder (VAE), Synthetic Minority Oversampling Technique (SMOTE), Data Synthesizer (DS), Synthetic Data Vault with Gaussian Copula (SDV-G), Conditional Generative Adversarial Networks (SDV-GAN), and SynthPop Non-Parametric (SP-NP) approach to synthesize data with regard to various datasets. We used the pairwise correlation and Synthetic Data (SD) metrics as utility measures respectively between real data and generated data for evaluation. Accordingly, this paper investigates the effects of various data generation models, and the processing time of every model is included as one of the evaluation metrics.

PreferenceSQL is an SQL extension for standard relational databases supporting soft constraints and is used to find relevant data intuitively. Meanwhile, the Semantic Web has interoperability advantages and helps to retrieve information with machine-readable data. We use the benefits of both technologies by combining preferences from SQL with SPARQL, the query language of the Semantic Web. This work provides implementation details in Apache Jena for the new composite called 'PreferenceSPARQL'. Furthermore, we contribute comprehensive benchmarks that show which preference algorithm is best suited for our approach.

The problem of Skyline computation has attracted considerable research attention in the last decade. A Skyline query selects those tuples from a dataset that are optimal with respect to a set of designated preference attributes. Since multicore processors are going mainstream, it has become imperative to develop parallel algorithms, which fully exploit the advantages of such modern hardware architectures. In this paper, the authors present high-performance parallel Skyline algorithms based on the lattice structure generated by a Skyline query. For this, they propose different evaluation strategies and compare several data structures for the parallel evaluation of Skyline queries. The authors present novel optimization techniques for lattice based Skyline algorithms based on pruning and removing one unrestricted attribute domain. They demonstrate through comprehensive experiments on synthetic and real datasets that their new algorithms outperform state-of-the-art multicore Skyline techniques for low-cardinality domains. The authors' algorithms have linear runtime complexity and fully play on modern hardware architectures.

SQL queries containing Group-by are common in data warehouse environments and OLAP. From this the concept of grouped Skyline queries emerged, wherein a Skyline of each group of tuples is requested. Grouped preference queries generalize this kind of Skyline queries. In this paper we present new algebraic transformation rules for grouped preference queries which are one of the most intuitive and practical type of queries. Our optimization laws reduce intermediate result sizes in the computation of joins, Cartesian products, and the preference selection. We have integrated these new rules into our rule-based Preference SQL query optimizer. Our performance benchmarks, building upon the well-known TPC-H and IMDB datasets, show that significant performance gains can be achieved.

Our demo application demonstrates a personalized location-based web application using Preference SQL that allows single users as well as groups of users to find accommodations in Istanbul that satisfy both hard constraints and user preferences. The application assists in defining spatial, numerical, and categorical base preferences and composes complex preference statements in an intuitive fashion. Unlike existing location-based services, the application considers spatial queries as soft instead of hard constraints to determine the best matches which are finally presented on a map. The underlying Preference SQL framework is implemented on top of a database, therefore enabling a seamless application integration with standard SQL back-end systems as well as efficient and extensible preference query processing.