Distributed Cache Table: Efficient Query-Driven Processing of Multi-Term Queries in P2P Networks

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The recent rise in interest in point clouds as an imaging modality has motivated standardization groups such as JPEG and MPEG to launch activities aiming at developing compression standards for point clouds. Lossy compression usually introduces visual arti ...

Analytical workloads are evolving as the number of users surges and applications that submit queries in batches become popular. However, traditional analytical databases that optimize-then-execute each query individually struggle to provide timely response ...

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Cloud function (CF) services, such as AWS Lambda, have been applied as the new computing infrastructure in implementing analytical query engines. For bursty and sparse workloads, CF-based query engine is more elastic than the traditional query engines runn ...

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Te ...

Natural language processing has experienced significant improvements with the development of Transformer-based models, which employ self-attention mechanism and pre-training strategies. However, these models still present several obstacles. A notable issue ...

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Recent advances in image compression have made it both possible and desirable for image quality to approach the visually lossless range. However, the most commonly used subjective visual quality assessment protocols, e.g. those reported in ITU-T Rec. BT.50 ...

Analytical engines rely on in-memory data caching to avoid storage accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- and time-based caching decisions, however, are a proxy of the expected query ...

Modern analytical engines rely on Approximate Query Processing (AQP) to provide faster response times than the hardware allows for exact query answering. However, existing AQP methods impose steep performance penalties as workload unpredictability increase ...

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Analytics on modern data analytic and data warehouse systems often need to run large complex queries on increasingly complex database schemas. A lot of progress has been made on executing such complex queries using techniques like scale out query processin ...

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Despite the increasing understanding of bentonite behaviour, there is still missing evidence on how different hydro-mechanical loadings, including sequences of hydration and compression, affect the fabric and the volume change behaviour of the material. It ...

Analytical engines rely on in-memory caching to avoid disk accesses and provide timely responses by keeping the most frequently accessed data in memory. Purely frequency- & time-based caching decisions, however, are a proxy of the expected query execution ...

Applications such as large-scale sparse linear algebra and graph analytics are challenging to accelerate on FPGAs due to the short irregular memory accesses, resulting in low cache hit rates. Nonblocking caches reduce the bandwidth required by misses by re ...

Due to the increasing number of pictures captured and stored every day by and on digital devices, lossy image compression has become inevitable to limit the needed storage requirement. As a consequence, these compression methods might introduce some visual ...

Modern data analytical workloads often need to run queries over a large number of tables. An optimal query plan for such queries is crucial for being able to run these queries within acceptable time bounds. However, with queries involving many tables, find ...

Distributed Cache Table: Efficient Query-Driven Processing of Multi-Term Queries in P2P Networks

Chattez avec Graph Search

Subjective performance evaluation of bitrate allocation strategies for MPEG and JPEG Pleno point cloud compression

Efficient Concurrent Analytical Query Processing using Data and Workload-conscious Sharing

Aggregation and Exploration of High-Dimensional Data Using the Sudokube Data Cube Engine

Interactive-time Exploration, Querying, and Analysis of Large High-dimensional Datasets

Using Cloud Functions as Accelerator for Elastic Data Analytics

The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

Modeling Structured Data in Attention-based Models

Evaluation of the impact of lossy compression on event camera-based computer vision tasks

On the assessment of high-quality images: advances on the JPEG AIC-3 activity

HPCache: memory-efficient OLAP through proportional caching revisited

LAQy: Efficient and Reusable Query Approximations via Lazy Sampling

On the Performance of Subjective Visual Quality Assessment Protocols for Nearly Visually Lossless Image Compression

Efficient GPU-accelerated Join Optimization for Complex Queries

The Universal Gossip Fighter

Graph Embedding for Retrieval

Volume change response and fabric evolution of granular MX80 bentonite along different hydro-mechanical stress paths

HPCache: Memory-Efficient OLAP Through Proportional Caching

Request, Coalesce, Serve, and Forget: Miss-Optimized Memory Systems for Bandwidth-Bound Cache-Unfriendly Applications on FPGAs

Towards JPEG AIC part 3: visual quality assessment of high to visually lossless image coding

Efficient Massively Parallel Join Optimization for Large Queries