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Machine learning (ML) techniques have been applied to high-level synthesis (HLS) flows for quality-of-result (QoR) prediction and design space exploration (DSE). Nevertheless, the scarcity of accessible high-quality HLS datasets and the…
This paper is an extended version of a report from a student-developed study to compare Microsoft SQL Server and PostgreSQL, two widely-used enterprise-class relational database management systems (RDBMSs). The study followed an…
Process event data is usually stored either in a sequential process event log or in a relational database. While the sequential, single-dimensional nature of event logs aids querying for (sub)sequences of events based on temporal relations…
Enterprise information systems have adopted Web-based foundations for exchanges between heterogeneous programmes. These programs provide and consume via Web APIs some resources identified by URIs, whose representations are transmitted via…
Modern big data systems run on cloud environments where resources are shared amongst several users and applications. As a result, declarative user queries in these environments need to be optimized and executed over resources that…
Scientific experiments, simulations, and modern applications generate large amounts of data. Data is stored in raw format to avoid the high loading time of traditional database management systems. Researchers have proposed many techniques…
NoSQL databases are widely used in modern applications due to their scalability and schema flexibility, yet they often rely on eventual consistency models that limit reliable transaction processing. This study proposes a four-stage…
Object-Centric Process Mining enables the analysis of complex operational behavior by capturing interactions among multiple business objects (e.g., orders, items, deliveries). These interactions are recorded using Object-Centric Event Data…
Scientific experiments and modern applications are generating large amounts of data every day. Most organizations utilize In-house servers or Cloud resources to manage application data and workload. The traditional database management…
Although the intention of RDF is to provide an open, minimally constraining way for representing information, there exists an increasing number of applications for which guarantees on the structure and values of an RDF data set become…
Modern high load applications store data using multiple database instances. Such an architecture requires data consistency, and it is important to ensure even distribution of data among nodes. Load balancing is used to achieve these goals.…
The DevOps paradigm is taking over software development systems, helping businesses increase efficiency, accelerate production, and adapt quickly to market changes. However, adopting these principles can be challenging. Practitioners often…
Data warehousing and OLAP applications must nowadays handle complex data that are not only numerical or symbolic. The XML language is well-suited to logically and physically represent complex data. However, its usage induces new theoretical…
Recently ontologies have been exploited in a wide range of research areas for data modeling and data management. They greatly assists in defining the semantic model of the underlying data combined with domain knowledge. In this paper, we…
The CMS experiment at the CERN LHC developed the Workflow Management Archive system to persistently store unstructured framework job report documents produced by distributed workflow management agents. In this paper we present its…
Data stores are the foundation on which data science, in all its variations, is built upon. They provide a queryable interface to structured and unstructured data. Data science often starts by leveraging these query features to perform…
The availability of network datasets advances research in network science, machine learning and related fields by enabling empirical analyses and their reproducibility, algorithm development, model validation and benchmarking. Existing…
The aim of this article is to present an overview of the major families of state-of-the-art data-base benchmarks, namely: relational benchmarks, object and object-relational benchmarks, XML benchmarks, and decision-support benchmarks, and…
Object Centric Event Data (OCED) has gained attention in recent years within the field of process mining. However, there are still many challenges, such as connecting the XES format to object-centric approaches to enable more insightful…
Network bound applications, like a database server executing OLTP queries or a caching server storing objects for a dynamic web applications, are essential services that consumers and businesses use daily. These services run on a large…