Approximate Query Processing: What is New and Where to Go?

Abstract

Online analytical processing (OLAP) is a core functionality in database systems. The performance of OLAP is crucial to make online decisions in many applications. However, it is rather costly to support OLAP on large datasets, especially big data, and the methods that compute exact answers cannot meet the high-performance requirement. To alleviate this problem, approximate query processing (AQP) has been proposed, which aims to find an approximate answer as close as to the exact answer efficiently. Existing AQP techniques can be broadly categorized into two categories. (1) Online aggregation: select samples online and use these samples to answer OLAP queries. (2) Offline synopses generation: generate synopses offline based on a-priori knowledge (e.g., data statistics or query workload) and use these synopses to answer OLAP queries. We discuss the research challenges in AQP and summarize existing techniques to address these challenges. In addition, we review how to use AQP to support other complex data types, e.g., spatial data and trajectory data, and support other applications, e.g., data visualization and data cleaning. We also introduce existing AQP systems and summarize their advantages and limitations. Lastly, we provide research challenges and opportunities of AQP. We believe that the survey can help the partitioners to understand existing AQP techniques and select appropriate methods in their applications.

Trading Accuracy for Performance in Data Processing Applications.

“…Some studies have proposed to apply precomputation in the context of Approximate Query Processing (AQP) in database systems [CDK17,LL18]. This consists of storing a summary of the source data with interesting information for the query (offline precomputation step) and then using this summary to approximate the source information and performing the query.…”

“…This consists of storing a summary of the source data with interesting information for the query (offline precomputation step) and then using this summary to approximate the source information and performing the query. However, there are not many solutions that use these techniques with complex data structures such as graph-based structures [CDK17,LL18]. In our case, performing precomputation with large models would be too costly, so we aim to perform the queries online, with no offline precomputation step.…”

Towards Automatic Mathematical Exercise Solving

“…Since knowledge graphs can provide well-structured information and relations of the entities, it is known to be useful to do reasoning in many tasks, such as query answering and relation inference (i.e. to infer missing relations in the knowledge graph [10,21,22]). Gu et al [15] proposed a technique to answer queries on knowledge graph by "compositionalizing" a broad class of vector space models, which performs well on query answering and knowledge graph completion.…”

Overlay Indexes: Efficiently Supporting Aggregate Range Queries and Authenticated Data Structures in Off-the-Shelf Databases

“…A largely investigated approach is called Approximate Query Processing (AQP), which aims at gaining efficiency while reducing the precision of query results. A survey of the achievements in this area is provided in [37]. This approach is relevant especially when data are large.…”