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Materialized views (MVs), stored pre-computed results, are widely used to facilitate fast queries on large datasets. When new records arrive at a high rate, it is infeasible to continuously update (maintain) MVs and a common solution is to…
Because the presence of views enhances query performance, materialized views are increasingly being supported by commercial database/data warehouse systems. Whenever the data warehouse is updated, the materialized views must also be…
Reusing intermediates in databases to speed-up analytical query processing has been studied in the past. Existing solutions typically require intermediate results of individual operators to be materialized into temporary tables to be…
Increasing resource demands require relational databases to scale. While relational databases are well suited for vertical scaling, specialized hardware can be expensive. Conversely, emerging NewSQL and NoSQL data stores are designed to…
In recent years, the surge in unstructured data analysis, facilitated by advancements in Machine Learning (ML), has prompted diverse approaches for handling images, text documents, and videos. Analysts, leveraging ML models, can extract…
Materialized view is used in large data centric applications to expedite query processing. The efficiency of materialized view depends on degree of result found against the queries over the existing materialized views. Materialized views…
Spatially-coupled (SC) codes, known for their threshold saturation phenomenon and low-latency windowed decoding algorithms, are ideal for streaming applications and data storage systems. SC codes are constructed by partitioning an…
A data warehouse is a large data repository for the purpose of analysis and decision making in organizations. To improve the query performance and to get fast access to the data, data is stored as materialized views (MV) in the data…
Continual learning algorithms which keep the parameters of new tasks close to that of previous tasks, are popular in preventing catastrophic forgetting in sequential task learning settings. However, 1) the performance for the new continual…
This article presents svds-C, an open-source and high-performance C program for accurately and robustly computing truncated SVD, e.g. computing several largest singular values and corresponding singular vectors. We have re-implemented the…
We study the accumulation of resources within a target due to the interplay between continual delivery, driven by 1D stochastic search processes, and sequential consumption. The assumption of sequential consumption is key because it changes…
Graph analytics are at the heart of a broad range of applications such as drug discovery, page ranking, and recommendation systems. When graph size exceeds memory size, out-of-core graph processing is needed. For the widely used external…
Graph databases are getting more and more attention in the highly interconnected data domain, and the demand for efficient querying of big data is increasing. We noticed that there are duplicate patterns in graph database queries, and the…
The scaling law, which indicates that model performance improves with increasing dataset and model capacity, has fueled a growing trend in expanding recommendation models in both industry and academia. However, the advent of large-scale…
Selectivity estimation remains a critical task in query optimization even after decades of research and industrial development. Optimizers rely on accurate selectivities when generating execution plans. They maintain a large range of…
Recent advancements in fields such as automotive and aerospace have driven a growing demand for robust computational resources. Applications that were once designed for basic MCUs are now deployed on highly heterogeneous SoC platforms.…
Mixed-Criticality (MC) systems consolidate multiple functionalities with different criticalities onto a single hardware platform. Such systems improve the overall resource utilization while guaranteeing resources to critical tasks. In this…
The growing demand for efficient cloud storage solutions has led to the widespread adoption of Solid-State Drives (SSDs) for caching in cloud block storage systems. The management of data writes to SSD caches plays a crucial role in…
With the need for flexible and on-demand decision support, Dynamic Data Warehouses (DDW) provide benefits over traditional data warehouses due to their dynamic characteristics in structuring and access mechanism. A DDW is a data framework…
Optimizing resource allocation for analytical workloads is vital for reducing costs of cloud-data services. At the same time, it is incredibly hard for users to allocate resources per query in serverless processing systems, and they…