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This paper investigates advanced storage models for evolving graphs, focusing on the efficient management of historical data and the optimization of global query performance. Evolving graphs, which represent dynamic relationships between…
Some aspects of Computer Algebra (notably Computation Group Theory and Computational Number Theory) have some good databases of examples, typically of the form "all the X up to size n". But most of the others, especially on the polynomial…
Large-scale storage cluster systems need to manage a vast amount of data locations. A naive data locations management maintains pairs of data ID and nodes storing the data in tables. However, it is not practical when the number of pairs is…
In today's world data is being generated at a high rate due to which it has become inevitable to analyze and quickly get results from this data. Most of the relational databases primarily support SQL querying with a limited support for…
Modern business applications and scientific databases call for inherently dynamic data storage environments. Such environments are characterized by two challenging features: (a) they have little idle system time to devote on physical…
Databases, and datasets more generally, evolve continuously through updates, transformations, versioning, schema changes, streaming operations, and other mechanisms. While prior work has noted connections among some of these areas, they…
The article deals with the problem which led to Big Data. Big Data information technology is the set of methods and means of processing different types of structured and unstructured dynamic large amounts of data for their analysis and use…
To stay competitive in today's data driven economy, enterprises large and small are turning to stream processing platforms to process high volume, high velocity, and diverse streams of data (fast data) as they arrive. Low-level programming…
Traditional enterprise warehouse solutions center around an analytical database system that is monolithic and inflexible: data needs to be extracted, transformed, and loaded into the rigid relational form before analysis. It takes years of…
It is increasingly common to find complex data represented through the graph model. Contrary to relational models, graphs offer a high capacity for executing analytical tasks on complex data. Since a huge amount of data is still presented…
The need for Knowledge and Data Discovery Management Systems (KDDMS) that support ad hoc data mining queries has been long recognized. A significant amount of research has gone into building tightly coupled systems that integrate…
Graph databases have grown in popularity in recent years as they are able to efficiently store and query complex relationships between data. Incidentally, navigation data and road networks can be processed, sampled or modified efficiently…
With the rise of big data, business intelligence had to find solutions for managing even greater data volumes and variety than in data warehouses, which proved ill-adapted. Data lakes answer these needs from a storage point of view, but…
Modern distributed systems often rely on so called weakly-consistent databases, which achieve scalability by sacrificing the consistency guarantee of distributed transaction processing. Such databases have been formalised in two different…
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…
This study aims to enhance data consistency in NoSQL databases, traditionally designed with BASE properties, as opposed to the strong consistency guaranteed by ACID principles in RDBMS. We introduce a comprehensive four-stage server-side…
A probabilistic database with attribute-level uncertainty consists of relations where cells of some attributes may hold probability distributions rather than deterministic content. Such databases arise, implicitly or explicitly, in the…
Due to the ever increasing importance of the internet, interoperability of heterogeneous data sources is as well of ever increasing importance. Interoperability can be achieved e.g. through data integration and data exchange. Common to both…
Students in introductory data management courses are often taught how to write queries in SQL. This is a useful and practical skill, but it gives limited insight into how queries are processed by relational database engines. In contrast,…
Relational databases play a central role in many information systems. Their schema contains structural (e.g. tables and columns) and behavioral (e.g. stored procedures or views) entity descriptions. Then, just like for ``normal'' software,…