- 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
Oct 31
2016
[DB Seminar] Fall 2016: Neil Shah
- Speaker:
- Neil Shah
Livestreaming platforms have become increasingly popular in recent years as a means of sharing and advertising creative content. Popular content streamers who attract large viewership to their live broadcasts can earn a living by means of ad revenue, donations and channel subscriptions. Unfortunately, this incentivized popularity has simultaneously resulted in incentive for fraudsters to provide services to astroturf, or artificially... Read More
Oct 24
2016
[DB Seminar] Fall 2016: Matteo Riondato (Two Sigma)
- Speaker:
- Matteo Riondato
TRIÈST is a suite of one-pass streaming algorithms to compute unbiased, low-variance, high- quality approximations of the global and local (i.e., incident to each vertex) number of triangles in a fully-dynamic graph represented as an adversarial stream of edge insertions and deletions. The algorithms use reservoir sampling and its variants to exploit the user-specified memory space at all times. This... Read More
Oct 19
2016
Charlie Swanson (MongoDB)
- Speaker:
- Charlie Swanson
- System:
- MongoDB
Everyone knows distributed systems are hard. At MongoDB we want to make it easy to express complex queries and extract insights from your data, but we also need to be able to scale to enormous data sets. To help you scale, we support a deployment which partitions the data amongst multiple machines, but a distributed system complicates even simple queries.... Read More
Oct 17
2016
[DB Seminar] Fall 2016: Round table discussion
We will have a round table discussion. Read More
Oct 14
2016
Jessie Li (Penn State)
- Speaker:
- Jessie Li
How could we harness the increasingly available big data to understand our dynamic ecosystem? For example, why people or animals move in the space in certain ways and how do their movements respond to surrounding environments? Why are crimes more frequent in certain regions and can we explain it using heterogeneous urban data? Is shale gas development contaminating our environment... Read More
Oct 10
2016
[DB Seminar] Fall 2016: Emaad Manzoor
- Speaker:
- Emaad Manzoor
Given a stream of heterogeneous graphs containing different types of nodes and edges, how can we spot anomalous ones in real-time while consuming bounded memory? This problem is motivated by and generalizes from its application in security to host-level advanced persistent threat (APT) detection. We propose StreamSpot, a clustering based anomaly detection approach that addresses challenges in two key fronts:... Read More
Oct 4
2016
Alex Robinson (CockroachDB)
- Speaker:
- Alex Robinson
- System:
- CockroachDB
Learn more about CockroachDB from Google alum and Member of Technical Staff, Alex Robinson! The talk will focus on how CockroachDB ensures data integrity, no matter how broadly distributed. Read More
Oct 3
2016
[DB Seminar] Fall 2016: Ashraf Aboulnaga (QCRI)
- Speaker:
- Ashraf Aboulnaga
Distributed data processing platforms such as Pregel and GraphLab have substantially simplified the design and deployment of certain classes of distributed graph analytics algorithms. However, these platforms do not represent a good match for distributed graph mining problems, for example, finding frequent subgraphs in a graph. Given an input graph, these problems require exploring a very large number of subgraphs... Read More
Sep 26
2016
[DB Seminar] Fall 2016: Wolfgang Gatterbauer (CMU)
- Speaker:
- Prof. Wolfgang Gatterbauer
Performing inference over large uncertain data sets is becoming a central data management problem. Recent large knowledge bases, such as Yago, Nell or DeepDive, have millions to billions of uncertain tuples. Because general reasoning under uncertainty is highly intractable, many state-of-the-art systems today perform approximate inference by reverting to sampling. This talk shows an alternative approach that allows approximate ranking... Read More
Sep 19
2016
[DB Seminar] Fall 2016: Prof. Shenghua Liu
- Speaker:
- Prof. Shenghua Liu
With mobile and web-based techniques to create highly interactive platforms, social media becomes prevalent in our daily life. It sees the interaction among people in which they create, share, discuss, or exchange ideas in virtual communities and networks. In this talk, he will introduce a series of his previous research work related to social media. They range from understanding short text, opinions,... Read More