- Aerospike
- Akamas
- AlloyDB
- ApertureDB
- Arrow
- 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
- Iceberg
- InfluxDB
- kdb
- ksqlDB
- LeanStore
- LMDB
- MapD
- Materialize
- Milvus
- MonetDB
- Mooncake
- MySQL
- Neon
- Noria
- OceanBase
- Oracle
- OxQL
- Pinecone
- PlanetScale
- PostgresML
- PRQL
- QMDB
- QuestDB
- Redshift
- RisingWave
- Rockset
- rqlite
- Samza
- SingleStore
- SLOG
- Snowflake
- SpiceDB
- SplinterDB
- SQL Server
- SQLite
- Stardog
- Striim
- Swarm64
- Technical University of Munich
- TiDB
- TileDB
- Tokutek
- Umbra
- Vertica
- VoltDB
- Weaviate
- XTDB
- YugabyteDB
- AirFlow
- Alibaba
- Anna
- APOLLO
- 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
- Hudi
- Impala
- Jepsen
- Kinetica
- LanceDB
- Litestream
- Malloy
- MariaDB
- MemSQL
- Modin
- MongoDB
- MotherDuck
- Napa
- NoisePage
- NuoDB
- OpenDAL
- OtterTune
- ParadeDB
- Pinot
- Polaris
- PostgreSQL
- Qdrant
- QuasarDB
- RavenDB
- RelationalAI
- RocksDB
- RonDB
- SalesForce
- ScyllaDB
- sled
- Smooth
- Spice.ai
- Splice Machine
- SQL Anywhere
- SQLancer
- SQream
- StarRocks
- Summingbird
- Synnada
- TerminusDB
- TigerBeetle
- TimescaleDB
- Trino
- Velox
- Vitesse
- Vortex
- WiredTiger
- Yellowbrick
- Aerospike
- Alibaba
- ApertureDB
- Azure Cosmos DB
- BlazingDB
- Cassandra
- Citus
- Confluent
- CrateDB
- DataFusion
- dbt
- Dolt
- DSQL
- EdgeDB
- eXtremeDB
- FeatureBase
- Firebolt
- FoundationDB
- GlareDB
- Greenplum
- Heron
- Impala
- kdb
- LanceDB
- LMDB
- MariaDB
- Milvus
- MongoDB
- MySQL
- NoisePage
- OceanBase
- OtterTune
- Pinecone
- Polaris
- PRQL
- QuasarDB
- Redshift
- RocksDB
- rqlite
- ScyllaDB
- SLOG
- Spice.ai
- SplinterDB
- SQLancer
- Stardog
- Summingbird
- Technical University of Munich
- TigerBeetle
- Tokutek
- Velox
- VoltDB
- WiredTiger
- YugabyteDB
- AirFlow
- AlloyDB
- APOLLO
- Berkeley DB
- Bodo
- Chaos Mesh
- ClickHouse
- Convex
- CrocodileDB
- Datometry
- Debezium
- Dremio
- DuckDB
- EraDB
- FASTER
- Featureform
- Fluree
- Gaia
- Google Spanner
- GreptimeDB
- Hudi
- InfluxDB
- Kinetica
- LeanStore
- Malloy
- Materialize
- Modin
- Mooncake
- Napa
- Noria
- OpenDAL
- OxQL
- Pinot
- PostgresML
- Qdrant
- QuestDB
- RelationalAI
- Rockset
- SalesForce
- SingleStore
- Smooth
- SpiceDB
- SQL Anywhere
- SQLite
- StarRocks
- Swarm64
- TerminusDB
- TileDB
- Trino
- Vertica
- Vortex
- XTDB
- Akamas
- Anna
- Arrow
- BigQuery
- Brytlyt
- Chroma
- CockroachDB
- CouchDB
- Databricks
- Datomic
- Delta Lake
- Druid
- DVMS
- Exon
- Fauna
- Feldera
- Fluss
- Gel
- GoogleSQL
- HarperDB
- Iceberg
- Jepsen
- ksqlDB
- Litestream
- MapD
- MemSQL
- MonetDB
- MotherDuck
- Neon
- NuoDB
- Oracle
- ParadeDB
- PlanetScale
- PostgreSQL
- QMDB
- RavenDB
- RisingWave
- RonDB
- Samza
- sled
- Snowflake
- Splice Machine
- SQL Server
- SQream
- Striim
- Synnada
- TiDB
- TimescaleDB
- Umbra
- Vitesse
- Weaviate
- Yellowbrick
- Aerospike
- AlloyDB
- Arrow
- BlazingDB
- Chaos Mesh
- CockroachDB
- CrateDB
- Datometry
- Delta Lake
- DSQL
- EraDB
- Fauna
- Firebolt
- Gaia
- GoogleSQL
- Heron
- InfluxDB
- ksqlDB
- LMDB
- Materialize
- MonetDB
- MySQL
- Noria
- Oracle
- Pinecone
- PostgresML
- QMDB
- Redshift
- Rockset
- Samza
- SLOG
- SpiceDB
- SQL Server
- Stardog
- Swarm64
- TiDB
- Tokutek
- Vertica
- Weaviate
- YugabyteDB
- AirFlow
- Anna
- Azure Cosmos DB
- Bodo
- Chroma
- Confluent
- CrocodileDB
- Datomic
- Dolt
- DuckDB
- Exon
- FeatureBase
- Fluree
- Gel
- Greenplum
- Hudi
- Jepsen
- LanceDB
- Malloy
- MemSQL
- MongoDB
- Napa
- NuoDB
- OtterTune
- Pinot
- PostgreSQL
- QuasarDB
- RelationalAI
- RonDB
- ScyllaDB
- Smooth
- Splice Machine
- SQLancer
- StarRocks
- Synnada
- TigerBeetle
- Trino
- Vitesse
- WiredTiger
- Akamas
- ApertureDB
- Berkeley DB
- Brytlyt
- Citus
- Convex
- Databricks
- dbt
- Dremio
- DVMS
- eXtremeDB
- Featureform
- Fluss
- GlareDB
- GreptimeDB
- Iceberg
- kdb
- LeanStore
- MapD
- Milvus
- Mooncake
- Neon
- OceanBase
- OxQL
- PlanetScale
- PRQL
- QuestDB
- RisingWave
- rqlite
- SingleStore
- Snowflake
- SplinterDB
- SQLite
- Striim
- Technical University of Munich
- TileDB
- Umbra
- VoltDB
- XTDB
- Alibaba
- APOLLO
- BigQuery
- Cassandra
- ClickHouse
- CouchDB
- DataFusion
- Debezium
- Druid
- EdgeDB
- FASTER
- Feldera
- FoundationDB
- Google Spanner
- HarperDB
- Impala
- Kinetica
- Litestream
- MariaDB
- Modin
- MotherDuck
- NoisePage
- OpenDAL
- ParadeDB
- Polaris
- Qdrant
- RavenDB
- RocksDB
- SalesForce
- sled
- Spice.ai
- SQL Anywhere
- SQream
- Summingbird
- TerminusDB
- TimescaleDB
- Velox
- Vortex
- Yellowbrick
Feb 22
2016
[DB Seminar] Spring 2016: Round table discussion
- Speaker:
- DB group members
This week, we will have round table discussion. We will talk about on-going research, and paper submissions. Read More
Feb 15
2016
[DB Seminar] Spring 2016: Wei Dai
- Speaker:
- Wei Dai
In this talk I will first give a brief overview of Petuum which encompasses a set of distributed machine learning principles as well as our open-sourced implementations. By discussing the the high level ideas and performance highlights, I hope to show that Big ML systems can benefit greatly from ML-rooted statistical and algorithmic insights. In the second part I will... Read More
Feb 8
2016
[DB Seminar] Spring 2016: Jun Woo Park
- Speaker:
- Jun Woo Park
Traditional sketches, such as the Bloom filter, the CountMin sketch, and the Space-Saving sketch, estimate set membership, frequency counts, or moments of scalar random variables. In this paper, we extend these approaches to a new family of sketches that approximate moments of vectorial random variables that satisfy convex polytope constraints. One application is the Semidefinite sketch, a succinct way to... Read More
Feb 1
2016
[DB Seminar] Spring 2016: Daniel Chino
- Speaker:
- Daniel Chino
Finding previously unknown patterns that frequently occur on time series is a core task of mining time series. These patterns are known as time series motifs and are essential to associate events and meaningful occurrences within the time series. In this work we propose a method based on a trie data structure, that allows a fast and accurate time series... Read More
Jan 25
2016
Jan 11
2016
Dec 14
2015
[DB Seminar] Fall 2015: Yifei Ma
- Speaker:
- Yifei Ma
Many modern information access problems involve highly complex patterns that cannot be handled by traditional keyword based search. Active Search is an emerging paradigm that helps users quickly find relevant information by efficiently collecting and learning from user feedback. We consider active search on graphs, where the nodes represent the set of instances users want to search over and the... Read More
Dec 7
2015
[DB Seminar] Fall 2015: Zeyuan Shang
- Speaker:
- Zeyuan Shang
Finding real-world applications and workloads is the bane of every database researcher. To overcome this problem, we present the Carnegie Mellon Database Application Catalog (DBAC). The DBAC finds database applications from on-line source code repositories (e.g., GitHub) and then automatically installs them in a virtual machine sandbox. It then submits requests to the application that cause it to interact with... Read More
Nov 30
2015
[DB Seminar] Fall 2015: Huanchen Zhang
- Speaker:
- Huanchen Zhang
Using indexes for query execution is crucial for achieving high performance in modern on-line transaction processing databases. For a main-memory database, however, these indexes consume a large fraction of the total memory available and are thus a major source of storage overhead of in-memory databases. To reduce this overhead, we propose using a two-stage index: The first stage ingests all... Read More
Nov 12
2015
Oracle Berkeley DB, an Open Source Embedded Key/Value Database System (Lauren Foutz)
- Speaker:
- Lauren Foutz
- System:
- Berkeley DB
- Video:
- YouTube
In 1991, graduate students at the University of California, Berkeley created an improved database engine library for Unix they named Berkeley DB (BDB). When the up and coming web browser company Netscape requested that the authors extend and improve the library, Sleepycat Software was born to maintain BDB. In the following decades BDB has been deployed millions of times and... Read More