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Database workloads have significantly evolved in the past twenty years. Traditional database systems that are mainly used to serve Online Transactional Processing (OLTP) workloads evolved into specialized database systems that are optimized for particular ...
While software applications, programming languages, and hardware have changed, operating systems have not. Widely-used commodity operating systems are still modeled after the ones designed in the seventies. The accumulated burden of backward compatibility ...
Software engineering practices have been steadily moving towards higher-level programming languages and away from lower-level ones. High-level languages tend to greatly improve safety, productivity, and code maintainability because they handle various impl ...
EPFL2020
Fuzzing reliably and efficiently finds bugs in software, including operating system kernels. In general, higher code coverage leads to the discovery of more bugs. This is why most existing kernel fuzzers adopt strategies to generate a series of inputs that ...
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 ...
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 ...
Grading student SQL queries manually is a tedious and error-prone process. Earlier work on testing correctness of student SQL queries, such as the XData system, can be used to test the correctness of a student query. However, in case a student query is fou ...
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 ...
The growing demand for data-intensive decision support and the migration to multi-tenant infrastructures put databases under the stress of high analytical query load. The requirement for high throughput contradicts the traditional design of query-at-a-time ...
Association for Computing Machinery2021
Computing the count of distinct elements in large data sets is a common task but naive approaches are memory-expensive. The HyperLogLog (HLL) algorithm (Flajolet et al., 2007) estimates a data set's cardinality while using significantly less memory than a ...