Oracle International Corporation

Linkedin Corporation

Workday, Inc.

Microsoft Technology Licensing, LLC

Srinivas Vemuri

Amit Ganesh

Bipul Sinha

Darren Kermit Lee

Maneesh Varshney

Juan R Loaiza

Rui Liu

Tirthankar Lahiri

Jonathan D Klein

Roger J Bamford

Lakshminarayanan Luxi Chidambaran

Srinath Krishnaswamy

Mehul Dilip Bastawala

Ashish Mittal

Kenneth Khiaw Hong Eng

Krishna P Puttaswamy Naga

Vineet Marwah

Namit Jain

Christof Bornhoevd

Dieter Gawlick

Vikram Joshi

Arvind Nithrakashyap

Mitul Tiwari

Kam Shergill

Atrayee Mullick

Michael John De Groot

Francis Wang

Ankita Kumar

Archna Kalra Johnson

Vinitha Reddy Gankidi

Eric W Bing

Himanshu Chatterjee

Mark Thompson

Wenlei Xie

Suvodeep Pyne

Subramanyam B Chitti

A system, method, and device for updating a base version of a hypercube are provided. The method includes (i) obtaining a request for a planning session with respect to a hypercube; and (ii) in response to obtaining the request: (a) determining that the planning session uses a base version of the hypercube, (b) receiving a value input to update a particular location in the hypercube, (c) storing the value for the particular location in the base version of the hypercube, and (d) propagating an update to the particular location of the base version of the hypercube to one or more scenario versions having a dependency on the base version.

What if scenario planning—updating a base plan version

A system, method, and device for indexing to a hypercube is provided. The method includes (i) obtaining an index that maps dimensional values to a collection of locations within a hypercube, (ii) determining that one or more tuples of the hypercube is updated along a particular dimension, and (iii) in response to determining that the one or more tuples of hypercube is updated along a particular dimension, creating a new virtual tuple for each updated row and updating the index to store a pointer to the new virtual tuple for a new particular dimension value, the virtual tuple comprising a pointer to a tuple in the index and a validity period for the tuple.

Memory efficient multi-versioned concurrency for a hypercube memory cache using virtual tuples

A system, method, and device for managing updates to a hypercube is provided. The method includes (i) obtaining a hypercube comprising a plurality of locations, wherein a particular location has an anchor object that stores version information for the particular location, and the particular location comprises a first formula that is evaluated to return a value for the particular location, (ii) in response to receiving the request for the value, determining that the particular location comprises the first formula, (iii) in response to determining that the particular location comprises the first formula, evaluating the first formula to obtain a result, and (iv) storing the result for the particular location in association with the version information for the particular location.

Memory efficient multi-versioned concurrency for a hypercube memory cache using formula snapshots

A method and apparatus for compression units in an index block is provided. A particular leaf block of a database index for a table is generated. The particular leaf block corresponds to a particular range of key values. A compression scheme is determined for a plurality of index entries with key values within the particular range. A compression unit that includes a plurality of compressed index entries is generated by applying the compression scheme to the plurality of index entries. Compression metadata describing the compression scheme is generated. The compression unit and the compression metadata is stored in the particular leaf block. In response to a request, at least a portion of the compression unit is decompressed to access one or more index entries.

Compression units in an index block

A query result set is an example of a type of body of data whose content is defined by a query. Modifications to a database can change the result of a query and therefore what the content of such a body of data, such as a result set, should be. Active Queries is an approach that enables a database server to detect database changes that alter the result of a query registered for a client and to notify a client of those changes. The notifications may be sent to, for example, a cache manager responsible for maintaining coherency of a result set cache generated by the query. The notification notifies the cache manager of when and/or what changes are needed to maintain the coherency of the cache.

Active queries filter extraction

Innovative techniques are disclosed for performing a combined sort and aggregation operation involving eagerly performing aggregation while sorting. The techniques described herein enable an aggregation and a group-by operation to be performed using an amount of system memory that is far less than the system memory required to store all the data records being processed while minimizing the need to spill data to disk. This combined sort and aggregation operation has better performance than conventional techniques since system memory is used more efficiently. In certain embodiments, a combined sort and aggregation operation is disclosed that enables the efficient sorting and aggregation of data records when the desired aggregation function is composable (such as SUM, COUNT, MIN and MAX aggregate operations).

Combined sort and aggregation

A method, system, and computer program product is disclosed for interacting with a client supported by a client-side cache. Embodiments of a method, a system, and a computer program product are disclosed that retrieve a first snapshot, indicating a state of the database after a last database request by the client, associated with the client, determine any number of invalid cached results for the client based on the first snapshot, and transmit the any number of invalid cached results and a second snapshot, an update for the first snapshot.

Server supporting a consistent client-side cache

This disclosure is directed to large-scale graph processing to determine second-degree connections for members of a social network. A social graph is duplicated into two graphs, where each of the two graphs are partitioned into various partitions. The partitions are each sorted according to a predetermined key selected from each of the graphs. The partitions are then assigned logical Work Units, where a first set of Work Units are determined from a first graph and second set of Work Units are determined from a second graph. The Work Units are determined to be asymmetrical such that the partitions of the first set of Work Units are assigned differently than the partitions of the second set of Work Units. One set of Work Units are loaded in-memory and another set of Work Units are streamed to a mapping module process, which determines the second-degree connections from the sets of Work Units.

Joins and aggregations on massive graphs using large-scale graph processing

A method, system, and computer program product is disclosed for caching results in a client-side cache. Embodiments of a method, a system, and a computer program product are disclosed that associate a first snapshot of a database with a client that indicates a state of the database after a last database request by the client, and indicate any number of invalid cached results in the client cache for the client based upon the first snapshot. In some embodiments, the method further includes receiving a second snapshot that indicates a state of the database upon receipt of a database server request by the client, and updating the first snapshot with the second snapshot.

Consistent client-side cache

A system, method, and apparatus are provided for organizing and joining massive sets of data (e.g., tens or hundreds of millions of event records). A dataset is Blocked by first identifying a partition key, which comprises one or more columns of the data. Each Block will contain all dataset records that have partition key values assigned to that Block. A cost constraint (e.g., a maximum size, a maximum number of records) may also be applied to the Blocks. A Block index is generated to identify all Blocks, their corresponding (sequential) partition key values, and their locations. A second dataset that includes the partition key column(s) and that must be correlated with the first dataset may then be Blocked according to the same ranges of partition key values (but without the cost constraint). Corresponding Blocks of the datasets may then be Joined/Aggregated, and analyzed as necessary.

Organizing, joining, and performing statistical calculations on massive sets of data

A system, method, and apparatus are provided for supporting and/or executing count-distinct queries. A large set of data (e.g., tens or hundreds of millions of event records) is condensed daily to generate presence bitmaps to reflect the distinctiveness of a selected data dimension S (e.g., user ID) for one or more key dimensions g1, g2, . . . (e.g., advertisement ID, campaign ID, advertiser ID). The condensation process eliminates duplication and yields a single value (e.g., 1 or 0) for each tuple [S, g1, . . . ] to represent the distinctiveness of each value in the S dimension to each combination of values in the grouping dimensions. On a monthly basis, the daily values are condensed to yield a single value for the month, and a similar process is applied on any other desired time granularities (e.g., year). The condensed data may be generated for any combination of selected dimension(s) and grouping dimension(s).

Method and system for configuring presence bitmaps identifying records with unique keys in a large data set

Method and system for performing count-distinct queries in a large data set that stores presence bitmaps corresponding to different time periods

Method and system for aggregating data in a large data set over a time period using presence bitmaps

Growing community of inventors

Santa Clara, CA, United States of America

Srinivas Vemuri