A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations | Paper Citation Record

Observation a7a4e7a5-f3f4-4421-a872-b4230b99bec5 · outbound

This paper cites Relational data-base management systems,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Relational data-base management systems,

Reference 1

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This paper cites Intel “big data.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Intel “big data

Reference 2

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Observation 20219a14-1e15-4d48-940e-7bbc2e351654 · outbound

This paper cites What goes around comes around... and around.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations What goes around comes around... and around

Reference 3

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This paper cites Quantum data management in the NISQ era,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Quantum data management in the NISQ era,

Reference 4

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This paper cites Plan stitch: Harnessing the best of many plans,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Plan stitch: Harnessing the best of many plans,

Reference 5

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This paper cites Neo: A learned query optimizer,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Neo: A learned query optimizer,

Reference 6

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Observation 18164161-0060-4c54-ad99-dda297e2c131 · outbound

This paper cites Simple adap- tive query processing vs. learned query optimizers: Observations and analysis,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Simple adap- tive query processing vs. learned query optimizers: Observations and analysis,

Reference 7

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Observation 28b663dc-af02-41d4-a00d-a5af39bdde5b · outbound

This paper cites Adaptive optimization of very large join queries,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Adaptive optimization of very large join queries,

Reference 8

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Observation fccd1047-fa91-43ba-a69f-3f0047a35b3d · outbound

This paper cites Spatial query optimization with learning,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Spatial query optimization with learning,

Reference 9

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Observation 83ea8571-98a7-4600-802e-a8a419a87abd · outbound

This paper cites Debunking the myth of join ordering: Toward robust SQL analytics,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Debunking the myth of join ordering: Toward robust SQL analytics,

Reference 10

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Observation a80647bd-01ec-4cd2-aa09-c828f5120485 · outbound

This paper cites Robust join processing with diamond hardened joins,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Robust join processing with diamond hardened joins,

Reference 11

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Observation ba037f72-6a4d-46bd-9b93-f5b6a03469e4 · outbound

This paper cites Output-optimal algorithms for join-aggregate queries,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Output-optimal algorithms for join-aggregate queries,

Reference 12

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Observation e296eb68-ed3e-46d1-b1f1-472f6b27f661 · outbound

This paper cites Optimizing queries with many-to-many joins,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Optimizing queries with many-to-many joins,

Reference 13

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This paper cites Test oracle,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Test oracle,

Reference 14

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This paper cites Theoretical and empirical studies of program testing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Theoretical and empirical studies of program testing,

Reference 15

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Observation 5b046c3a-b693-4e3a-a387-53d724a373ce · outbound

This paper cites SQLSmith,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations SQLSmith,

Reference 16

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Observation b8e72e5f-1113-4c8c-884d-63aaafc1c650 · outbound

This paper cites American fuzzy lop,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations American fuzzy lop,

Reference 17

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Observation 1f878f5b-5cef-49c0-8480-c067b6bfa7f3 · outbound

This paper cites Massive stochastic testing of SQL,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Massive stochastic testing of SQL,

Reference 18

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Observation d7ec5629-3302-4cde-8e58-4f51cdc6c043 · outbound

This paper cites Testing database engines via pivoted query synthesis,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Testing database engines via pivoted query synthesis,

Reference 19

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This paper cites Finding bugs in database systems via query partitioning,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Finding bugs in database systems via query partitioning,

Reference 20

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Observation dc38bdd5-6086-4fd0-8a88-f8444bb05dcc · outbound

This paper cites THANOS: DBMS bug detection via storage engine rotation based differential testing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations THANOS: DBMS bug detection via storage engine rotation based differential testing,

Reference 21

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Observation 28b64969-9406-4f8a-b9ed-16d5d8df9e66 · outbound

This paper cites Detecting optimization bugs in database engines via non-optimizing reference engine construction,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Detecting optimization bugs in database engines via non-optimizing reference engine construction,

Reference 22

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Observation 3cb85ded-ba1c-48ae-948c-3c484b5bc883 · outbound

This paper cites Detecting schema-related logic bugs in relational DBMSs via equiv- alent database construction,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Detecting schema-related logic bugs in relational DBMSs via equiv- alent database construction,

Reference 23

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Observation cdf48776-b5ff-409a-af9e-40292f535ef6 · outbound

This paper cites Detecting logic bugs of join optimizations in DBMS,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Detecting logic bugs of join optimizations in DBMS,

Reference 24

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Observation 6d46c886-f27b-4f45-9935-07f4373f247e · outbound

This paper cites Keep it simple: Testing databases via differential query plans,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Keep it simple: Testing databases via differential query plans,

Reference 25

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This paper cites an unresolved cited work.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Unresolved cited work

Reference 26

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Observation ea81d7d3-2075-453c-ac4a-8b7b5da0a54d · outbound

This paper cites SQLancer,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations SQLancer,

Reference 27

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Observation 405a80af-3180-4593-9574-2be61789cb88 · outbound

This paper cites A relational model of data for large shared data banks,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations A relational model of data for large shared data banks,

Reference 28

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Observation 722f60a0-a657-4f9a-bd5a-be44a0cf7734 · outbound

This paper cites Access path selection in a relational database management system,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Access path selection in a relational database management system,

Reference 29

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This paper cites The volcano optimizer generator: Exten- sibility and efficient search,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations The volcano optimizer generator: Exten- sibility and efficient search,

Reference 30

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Observation cba24fe1-5c52-47cc-9699-c18d74e8c11b · outbound

This paper cites An overview of query optimization in relational systems,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations An overview of query optimization in relational systems,

Reference 31

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Observation 2c893c26-8e5c-489f-9ccf-9b362174492b · outbound

This paper cites Query optimization through the looking glass, and what we found running the join order benchmark,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Query optimization through the looking glass, and what we found running the join order benchmark,

Reference 32

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Observation c3311ff8-4bef-4c55-ba94-a25cc1fc678c · outbound

This paper cites Metamorphic Testing: A New Approach for Generating Next Test Cases.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Metamorphic Testing: A New Approach for Generating Next Test Cases

Reference 33

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A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Metamorphic testing: A review of challenges and opportunities,

Reference 34

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This paper cites A survey on metamorphic testing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations A survey on metamorphic testing,

Reference 35

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This paper cites Richard Hipp speaks out on SQLite,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Richard Hipp speaks out on SQLite,

Reference 36

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This paper cites CERT: Finding performance issues in database systems through the lens of cardinality estimation,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations CERT: Finding performance issues in database systems through the lens of cardinality estimation,

Reference 37

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This paper cites QAGen: generating query-aware test databases,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations QAGen: generating query-aware test databases,

Reference 38

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This paper cites Flexible database generators,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Flexible database generators,

Reference 39

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This paper cites Quickly generating billion-record synthetic databases,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Quickly generating billion-record synthetic databases,

Reference 40

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A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Simple and realistic data gener- ation,

Reference 41

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This paper cites Query- aware test generation using a relational constraint solver,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Query- aware test generation using a relational constraint solver,

Reference 42

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A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations A genetic approach for random testing of database systems,

Reference 43

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This paper cites Generating queries with cardinality constraints for DBMS testing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Generating queries with cardinality constraints for DBMS testing,

Reference 44

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This paper cites Apollo: Automatic detection and diagnosis of performance regressions in database systems,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Apollo: Automatic detection and diagnosis of performance regressions in database systems,

Reference 45

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This paper cites Generating targeted queries for database testing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Generating targeted queries for database testing,

Reference 46

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This paper cites Generating thousand benchmark queries in seconds,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Generating thousand benchmark queries in seconds,

Reference 47

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A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations QRelX: generating meaningful queries that provide cardinality assurance,

Reference 48

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This paper cites Testing the accuracy of query optimizers,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Testing the accuracy of query optimizers,

Reference 49

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This paper cites Automated SQL query generation for systematic testing of database engines,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Automated SQL query generation for systematic testing of database engines,

Reference 50

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This paper cites A framework for testing DBMS features,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations A framework for testing DBMS features,

Reference 51

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This paper cites Snowtrail: Testing with production queries on a cloud database,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Snowtrail: Testing with production queries on a cloud database,

Reference 52

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This paper cites SQUIRREL: Testing database management systems with language validity and cov- erage feedback,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations SQUIRREL: Testing database management systems with language validity and cov- erage feedback,

Reference 53

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This paper cites REDQUEEN: Fuzzing with input-to-state correspondence,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations REDQUEEN: Fuzzing with input-to-state correspondence,

Reference 54

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This paper cites EnFuzz: Ensemble fuzzing with seed synchronization among diverse fuzzers,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations EnFuzz: Ensemble fuzzing with seed synchronization among diverse fuzzers,

Reference 55

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This paper cites DeepFuzzer: Accelerated deep greybox fuzzing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations DeepFuzzer: Accelerated deep greybox fuzzing,

Reference 56

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This paper cites PATA: Fuzzing with path aware taint analysis,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations PATA: Fuzzing with path aware taint analysis,

Reference 57

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This paper cites RIFF: Reduced instruction footprint for Coverage-Guided fuzzing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations RIFF: Reduced instruction footprint for Coverage-Guided fuzzing,

Reference 58

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This paper cites Industry practice of coverage-guided enterprise-level DBMS fuzzing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Industry practice of coverage-guided enterprise-level DBMS fuzzing,

Reference 59

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Observation abc4f2e3-c8f0-4095-8198-a89bfe76f57f · outbound

This paper cites Unicorn: detect runtime errors in time-series databases with hybrid input synthesis,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Unicorn: detect runtime errors in time-series databases with hybrid input synthesis,

Reference 60

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This paper cites QSYM: A practical concolic execution engine tailored for hybrid fuzzing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations QSYM: A practical concolic execution engine tailored for hybrid fuzzing,

Reference 61

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Observation c21cbfd9-0080-4e34-9291-f777b0f9f5ef · outbound

This paper cites Sequence-oriented DBMS fuzzing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Sequence-oriented DBMS fuzzing,

Reference 62

Resolution: unresolved; no resolver link, observed 2026-06-26T06:26:43.244268Z

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Observation dc3d668d-dee1-4c4c-8120-18fe9e9f90c8 · outbound

This paper cites Griffin : Grammar-free DBMS fuzzing,.

A Set-Theoretic Approach to Detecting Logic Bugs in DBMS Inner Join Optimizations Griffin : Grammar-free DBMS fuzzing,

Reference 63

Resolution: unresolved; no resolver link, observed 2026-06-26T06:26:43.244268Z

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Unavailable: canonical work link unavailable.

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