Turn Inc.

Facebook, Inc.

Nec Laboratories America, Inc.

Songting Chen

Junichi Tatemura

Mingxi Wu

Wang-Pin Hsiung

Ping Chen

Purbesh Sahoo

Luping Ding

Elke A Rundensteiner

Yi Cheng

Ruichao Qiu

A database search method combines SQL command, complex event processing, and keyword search. Event data records may be collected based on the events specified in a query. A complex event processing analysis is used to determine the records that meet the correct sequence of events. The keyword search is used to filter user data records that have the attributes specified in the query. A SQL command may be used to further analyze or group the data records found. One or more metrics of the filtered results are presented as the result of the query. To run a new query efficiently, user identifiers are distributed using hashes. Adaptive sampling is used to determine whether the search result is within a desired margin of error. An efficient search for a specific condition is performed using a pace computation that jumps into a location of user data records that have the attributes.

Audience-centric event analysis

A generic approach for automatically ingesting data into an HDFS (Hadoop File System) based data warehouse includes a datahub server, a generic pipelined data loading framework, and a meta-data model that, together, address data loading efficiency, data source heterogeneities, and data warehouse schema evolvement. The loading efficiency is achieved via the MapReduce scale-out solution. The meta-data model is comprised of configuration files and a catalog. The configuration file is setup per ingestion task. The catalog manages the data warehouse schema. When a scheduled data loading task is executed, the configuration files and the catalog collaboratively drive the datahub server to load the heterogeneous data to their destination schemas automatically.

Meta-data driven data ingestion using MapReduce framework

A method and apparatus for a data warehouse built on top of MapReduce is provided. A virtual view is defined on top of the common star or snowflake data warehouse schema. A SQL-like, but much more succinct, query language is provided to leverage the virtual view. Many advanced query processing features leveraging the virtual view are also provided. Further, several optimization techniques ranging from data compression and access method to multi-query optimization and exploiting materialized views are provided. Further, a technique to seamlessly integrate the data warehouse system into any ad-hoc MapReduce jobs is provided. Thus, fully leveraging the power of both MapReduce and data warehouse technologies is achieved.

Custom data warehouse on top of mapreduce

Systems and method are disclosed for applying a query to an event stream by storing one or more event constraints; performing constraint aware complex event processing on the query and the event constraints; and optimizing the query at run time.

Runtime semantic query optimization for event stream processing

Growing community of inventors

San Jose, CA, United States of America

Songting Chen