Publications de Ioannis Alagiannis

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Nowadays, business and scientific applications accumulate data at an increasing pace. This growth of information has already started to outgrow the capabilities of database management systems (DBMS). In a typical DBMS usage scenario, the user should define ...

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare and to load the data into the database before executing the desired queries. Many applications already av ...

Modern big data workflows, found in e.g., machine learning use cases, often involve iterations of cycles of batch analytics and interactive analytics on temporary data. Whereas batch analytics solutions for large volumes of raw data are well established (e ...

Modern state-of-the-art database systems are designed around a single data storage layout. This is a fixed decision that drives the whole architectural design of a database system, i.e., row-stores, column-stores. However, none of those choices is a univer ...

As data collections become larger and larger, data loading evolves to a major bottleneck. Many applications already avoid using database systems, e.g., scientific data analysis and social networks, due to the complexity and the increased data-to-query time ...

A database system maintains a feature set of a modern database system while operating directly on raw data files. Systems may use an adaptive indexing mechanism that maintains positional information to provide efficient access to raw data files, a flexible ...

The explosion of available data in the last few years has increased the importance of physical database design, since the selection of proper physical structures (e.g. indices, partitions and materialized views) may improve query execution performance by s ...

As data collections become larger and larger, users are faced with increasing bottlenecks in their data analysis. More data means more time to prepare the data, to load the data into the database and to execute the desired queries. Many applications alread ...

The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in ...

As the size of data and its heterogeneity increase, traditional database system architecture becomes an obstacle to data analysis. Integrating and ingesting (loading) data into databases is quickly becoming a bottleneck in face of massive data as well as i ...

Advances in observation instruments and abundance of computational power for simulations encourage scientists to gather and produce unprecedented amounts of increasingly complex data. Organizing data automatically to enable efficient and unobstructed acces ...

Database systems deliver impressive performance for large classes of workloads as the result of decades of research into optimizing database engines. High performance, however, is achieved at the cost of versatility. In particular, database systems only op ...

Industry and academia are continuously becoming more data-driven and data-intensive, relying on the analysis of a wide variety of heterogeneous datasets to gain insights. The different data models and formats pose a significant challenge on performing anal ...

As data analytics is used by an increasing number of applications, data analytics engines are required to execute workloads with increased concurrency, i.e., an increasing number of clients submitting queries. Data management systems designed for data anal ...

Ioannis Alagiannis

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Adaptive Query Processing on Raw Data Files

NoDB: Efficient Query Execution on Raw Data Files

DiNoDB: Efficient Large-Scale Raw Data Analytics

H2O: A Hands-free Adaptive Store

NoDB: Efficient Query Execution on Raw Data Files

Query management system and engine allowing for efficient query execution on raw details

Automated Physical Designers: What You See is (Not) What You Get

NoDB in Action: Adaptive Query Processing on Raw Data

Adaptive partitioning and indexing for in situ query processing

Just-In-Time Data Virtualization: Lightweight Data Management with ViDa

Challenges and Opportunities in Self-Managing Scientific Databases

Adaptive Query Processing on RAW Data

Fast Queries Over Heterogeneous Data Through Engine Customization

Scaling up analytical queries with column-stores

DBMS Data Loading: An Analysis on Modern Hardware