- Aerospike
- Akamas
- AlloyDB
- ApertureDB
- Arrow
- Azure Cosmos DB
- BigQuery
- Bodo
- Cassandra
- Chroma
- ClickHouse
- Confluent
- CouchDB
- CrocodileDB
- DataFusion
- Datomic
- Debezium
- Dolt
- Druid
- DuckDB
- EdgeDB
- Exon
- FASTER
- FeatureBase
- Feldera
- Fluree
- FoundationDB
- Gel
- Google Spanner
- Greenplum
- HarperDB
- HorizonDB
- Iceberg
- InfluxDB
- kdb
- ksqlDB
- LeanStore
- LMDB
- MapD
- Materialize
- Milvus
- MonetDB
- Mooncake
- Multigres
- Napa
- NoisePage
- NuoDB
- OpenDAL
- OtterTune
- OxQL
- Pinecone
- Pixeltable
- Polaris
- PostgreSQL
- Qdrant
- QuasarDB
- RavenDB
- RelationalAI
- RocksDB
- RonDB
- SalesForce
- ScyllaDB
- sled
- Smooth
- SpacetimeDB
- SpiceDB
- SplinterDB
- SQL Server
- SQLite
- Stardog
- Striim
- Swarm64
- Technical University of Munich
- TiDB
- TileDB
- Tokutek
- TopK
- turbopuffer
- Velox
- VillageSQL
- VoltDB
- Weaviate
- XTDB
- YugabyteDB
- AirFlow
- Alibaba
- Anna
- APOLLO
- Aurora DSQL
- Berkeley DB
- BlazingDB
- Brytlyt
- Chaos Mesh
- Citus
- CockroachDB
- Convex
- CrateDB
- Databricks
- Datometry
- dbt
- Delta Lake
- Dremio
- DSQL
- DVMS
- EraDB
- eXtremeDB
- Fauna
- Featureform
- Firebolt
- Fluss
- Gaia
- GlareDB
- GoogleSQL
- GreptimeDB
- Heron
- Hudi
- Impala
- Jepsen
- Kinetica
- LanceDB
- Litestream
- Malloy
- MariaDB
- MemSQL
- Modin
- MongoDB
- MotherDuck
- MySQL
- Neon
- Noria
- OceanBase
- Oracle
- Oxla
- ParadeDB
- Pinot
- PlanetScale
- PostgresML
- PRQL
- QMDB
- QuestDB
- Redshift
- RisingWave
- Rockset
- rqlite
- Samza
- SingleStore
- SLOG
- Snowflake
- Spice.ai
- Splice Machine
- SQL Anywhere
- SQLancer
- SQream
- StarRocks
- Summingbird
- Synnada
- TerminusDB
- TigerBeetle
- TimescaleDB
- TonicDB
- Trino
- Umbra
- Vertica
- Vitesse
- Vortex
- WiredTiger
- Yellowbrick
- Aerospike
- Alibaba
- ApertureDB
- Aurora DSQL
- BigQuery
- Brytlyt
- Chroma
- CockroachDB
- CouchDB
- Databricks
- Datomic
- Delta Lake
- Druid
- DVMS
- Exon
- Fauna
- Feldera
- Fluss
- Gel
- GoogleSQL
- HarperDB
- Hudi
- InfluxDB
- Kinetica
- LeanStore
- Malloy
- Materialize
- Modin
- Mooncake
- MySQL
- NoisePage
- OceanBase
- OtterTune
- ParadeDB
- Pixeltable
- PostgresML
- Qdrant
- QuestDB
- RelationalAI
- Rockset
- SalesForce
- SingleStore
- Smooth
- Spice.ai
- SplinterDB
- SQLancer
- Stardog
- Summingbird
- Technical University of Munich
- TigerBeetle
- Tokutek
- Trino
- Velox
- Vitesse
- Weaviate
- Yellowbrick
- AirFlow
- AlloyDB
- APOLLO
- Azure Cosmos DB
- BlazingDB
- Cassandra
- Citus
- Confluent
- CrateDB
- DataFusion
- dbt
- Dolt
- DSQL
- EdgeDB
- eXtremeDB
- FeatureBase
- Firebolt
- FoundationDB
- GlareDB
- Greenplum
- Heron
- Iceberg
- Jepsen
- ksqlDB
- Litestream
- MapD
- MemSQL
- MonetDB
- MotherDuck
- Napa
- Noria
- OpenDAL
- Oxla
- Pinecone
- PlanetScale
- PostgreSQL
- QMDB
- RavenDB
- RisingWave
- RonDB
- Samza
- sled
- Snowflake
- SpiceDB
- SQL Anywhere
- SQLite
- StarRocks
- Swarm64
- TerminusDB
- TileDB
- TonicDB
- turbopuffer
- Vertica
- VoltDB
- WiredTiger
- YugabyteDB
- Akamas
- Anna
- Arrow
- Berkeley DB
- Bodo
- Chaos Mesh
- ClickHouse
- Convex
- CrocodileDB
- Datometry
- Debezium
- Dremio
- DuckDB
- EraDB
- FASTER
- Featureform
- Fluree
- Gaia
- Google Spanner
- GreptimeDB
- HorizonDB
- Impala
- kdb
- LanceDB
- LMDB
- MariaDB
- Milvus
- MongoDB
- Multigres
- Neon
- NuoDB
- Oracle
- OxQL
- Pinot
- Polaris
- PRQL
- QuasarDB
- Redshift
- RocksDB
- rqlite
- ScyllaDB
- SLOG
- SpacetimeDB
- Splice Machine
- SQL Server
- SQream
- Striim
- Synnada
- TiDB
- TimescaleDB
- TopK
- Umbra
- VillageSQL
- Vortex
- XTDB
- Aerospike
- AlloyDB
- Arrow
- BigQuery
- Cassandra
- ClickHouse
- CouchDB
- DataFusion
- Debezium
- Druid
- EdgeDB
- FASTER
- Feldera
- FoundationDB
- Google Spanner
- HarperDB
- Iceberg
- kdb
- LeanStore
- MapD
- Milvus
- Mooncake
- Napa
- NuoDB
- OtterTune
- Pinecone
- Polaris
- Qdrant
- RavenDB
- RocksDB
- SalesForce
- sled
- SpacetimeDB
- SplinterDB
- SQLite
- Striim
- Technical University of Munich
- TileDB
- TopK
- Velox
- VoltDB
- XTDB
- AirFlow
- Anna
- Aurora DSQL
- BlazingDB
- Chaos Mesh
- CockroachDB
- CrateDB
- Datometry
- Delta Lake
- DSQL
- EraDB
- Fauna
- Firebolt
- Gaia
- GoogleSQL
- Heron
- Impala
- Kinetica
- Litestream
- MariaDB
- Modin
- MotherDuck
- Neon
- OceanBase
- Oxla
- Pinot
- PostgresML
- QMDB
- Redshift
- Rockset
- Samza
- SLOG
- Spice.ai
- SQL Anywhere
- SQream
- Summingbird
- TerminusDB
- TimescaleDB
- Trino
- Vertica
- Vortex
- Yellowbrick
- Akamas
- ApertureDB
- Azure Cosmos DB
- Bodo
- Chroma
- Confluent
- CrocodileDB
- Datomic
- Dolt
- DuckDB
- Exon
- FeatureBase
- Fluree
- Gel
- Greenplum
- HorizonDB
- InfluxDB
- ksqlDB
- LMDB
- Materialize
- MonetDB
- Multigres
- NoisePage
- OpenDAL
- OxQL
- Pixeltable
- PostgreSQL
- QuasarDB
- RelationalAI
- RonDB
- ScyllaDB
- Smooth
- SpiceDB
- SQL Server
- Stardog
- Swarm64
- TiDB
- Tokutek
- turbopuffer
- VillageSQL
- Weaviate
- YugabyteDB
- Alibaba
- APOLLO
- Berkeley DB
- Brytlyt
- Citus
- Convex
- Databricks
- dbt
- Dremio
- DVMS
- eXtremeDB
- Featureform
- Fluss
- GlareDB
- GreptimeDB
- Hudi
- Jepsen
- LanceDB
- Malloy
- MemSQL
- MongoDB
- MySQL
- Noria
- Oracle
- ParadeDB
- PlanetScale
- PRQL
- QuestDB
- RisingWave
- rqlite
- SingleStore
- Snowflake
- Splice Machine
- SQLancer
- StarRocks
- Synnada
- TigerBeetle
- TonicDB
- Umbra
- Vitesse
- WiredTiger
Dec 3
2019
[PDL/SDI] Fall 2019: Hideaki Kimura (Oracle)
- Speaker:
- Hideaki Kimura
- System:
- Oracle
This talk gives an overview of Oracle NVM Direct, an open source implementation of a C API to simplify application development for byte addressable Non Volatile Memory. NVM Direct consists of two major components, namely: A precompiler that converts a source file containing C extensions for NVM to a standard C source file. A runtime library to implement NVM regions,... Read More
Dec 2
2019
[DB Seminar] Fall 2019 DB Group: Shasank Chavan (Oracle)
- Speaker:
- Shasank Chavan
- System:
- Oracle
Autonomous / Self-Driving Databases utilize machine learning techniques to eliminate the manual labor associated with database tuning, security, backups, updates, and other routine management tasks traditionally performed by DBAs. This talk will focus specifically on how we implement a self-performing database with Oracle’s Database In-Memory product to automatically tune for query optimization, memory management, and storage management and data tiering.... Read More
Nov 25
2019
[DB Seminar] Fall 2019 DB Group: Mesh: Automatically Compacting Your C++ Application’s Memory
In this DB group meeting, we are going to watch this presentation by Emery Berger on Mesh from PLDI 2019. This is legit. Read More
Nov 18
2019
[DB Seminar] Fall 2019 DB Group: Transactions and Scalability in Cloud Databases—Can’t We Have Both?
- Speaker:
- Doug Terry
In this DB group meeting, we are going to watch this presentation by the Amazon AWS group given at FAST 2019. Read More
Nov 11
2019
[DB Seminar] Fall 2019 DB Group: Huzaifa Abbasi
- Speaker:
- Huzaifa Abbasi
Huzaifa will present this paper in this meeting: Title: Scalable Garbage Collection for In-Memory MVCC Systems, VLDB 2020 Read More
Oct 21
2019
[DB Seminar] Fall 2019 DB Group: Gustavo Angulo
- Speaker:
- Gustavo Angulo
Gus will present this paper in this meeting: Title: How I Learned to Stop Worrying and Love Re-optimization, Arxiv 2019 Read More
Oct 14
2019
[DB Seminar] Fall 2019 DB Group: Dongsheng Yang
- Speaker:
- Dongsheng Yang
Dongsheng will present this paper in this meeting: Title: An End-to-End Automatic Cloud Database Tuning System Using Deep Reinforcement Learning: SIGMOD 2019 Read More
Oct 11
2019
Fall 2019: Vagelis Papalexakis (UC Riverside)
- Speaker:
- Vagelis Papalexakis
Tensors and tensor decompositions have been very popular and effective tools for analyzing multi-aspect data in a wide variety of fields, ranging from Psychology to Chemometrics, and from Signal Processing to Data Mining and Machine Learning. Using tensors in the era of big data presents us with a rich variety of applications, but also poses great challenges, especially when it... Read More
Oct 7
2019
[DB Seminar] Fall 2019 DB Group: Matt Butrovich
- Speaker:
- Matt Butrovich
Matt will present this paper in this meeting: Title: Adapting TPC-C Benchmark to Measure Performance of Multi-Document Transactions in MongoDB: VLDB 2019 Read More
Oct 4
2019
PhD Defense: Huanchen Zhang
- Speaker:
- Huanchen Zhang
The growing cost gap between DRAM and storage together with increasing database sizes means that database management systems (DBMSs) now operate with a lower memory to storage size ratio than before. On the other hand, modern DBMSs rely on in-memory search trees (e.g., indexes and filters) to achieve high throughput and low latency. These search trees, however, consume a large... Read More