Irena Holubová

Jáchym Bártík, Alžběta Šrůtková, Irena Holubová

Contemporary information systems operate in heterogeneous and continuously evolving data environments, where representation choices and structural redesign decisions strongly influence system behavior. Existing benchmarking approaches, however, rely mostly on static datasets and fixed schemas, providing limited support for analyzing architectural trade-offs or guiding evolution in multi-model settings. This paper introduces TransforMMer, a framework for evolution-aware and representation-aware benchmark engineering in heterogeneous information systems. The approach treats benchmark construction as a systematic design process: starting from raw data, inferring structure, refining it conceptually, and generating comparable dataset variants across relational, document, and graph systems. The framework is grounded in a unified representation that enables explicit modeling of schemas and cross-model mappings and supports reproducible transformations across alternative representations. We position benchmarking as a system-design tool for evaluating architectural and representation-level decisions in evolving information systems, rather than as a static comparison of database engines. Through controlled benchmark construction scenarios on real-world datasets, we demonstrate how structural redesign steps -- such as embedding, enrichment, and hybrid partitioning -- affect observed query costs across systems. The results show that performance differences emerge primarily from the interaction between workload and representation design. By enabling systematic generation of structurally distinct yet semantically aligned dataset variants, the proposed approach connects conceptual data modeling with empirical system evaluation and supports reproducible, evolution-aware analysis of heterogeneous information systems.

Angela Bonifati, Irena Holubová, Arnau Prat-Pérez et al.

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.

Jakub Kúdela, Irena Holubová, Ondřej Bojar

Most of the current methods for mining parallel texts from the web assume that web pages of web sites share same structure across languages. We believe that there still exists a non-negligible amount of parallel data spread across sources not satisfying this assumption. We propose an approach based on a combination of bivec (a bilingual extension of word2vec) and locality-sensitive hashing which allows us to efficiently identify pairs of parallel segments located anywhere on pages of a given web domain, regardless their structure. We validate our method on realigning segments from a large parallel corpus. Another experiment with real-world data provided by Common Crawl Foundation confirms that our solution scales to hundreds of terabytes large set of web-crawled data.