Titlebook: In Memory Data Management and Analysis; First and Second Int Arun Jagatheesan,Justin Levandoski,Andrew Pavlo Conference proceedings 2015 Sp

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Compiled Plans for In-Memory Path-Counting Queriesng but sacrifice the ability to express basic relational idioms. However, we hypothesize that the performance benefits amount to implementation details, not a fundamental limitation of the relational model. To evaluate this hypothesis, we are exploring code-generation to produce fast in-memory algor

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Bringing Linear Algebra Objects to Life in a Column-Oriented In-Memory Databaseng. Common applications include eigenvalue determination of large matrices, which decompose into a set of linear algebra operations. With the rise of in-memory databases it is now feasible to execute these complex analytical queries directly in a relational database system without the need of transf

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Dynamic Query Prioritization for In-Memory Databasess while executing business transactions in parallel. The main reasons for this increase of performance are massive intra-query parallelism on many-core CPUs and primary data storage in main memory instead of disks or SSDs. However, database management systems in enterprise scenarios typically run a

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Aggregates Caching in Columnar In-Memory Databases-intensive data aggregations. In this context, caching the query results of long-running queries is desirable as it increases the overall performance. However, traditional caching approaches are inefficient in a way that changes in the base data result in invalidation or recalculation of cached resu

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