Microsoft Technology Licensing, LLC

International Business Machines Corporation

Ahmed Eldawy

Kun-Lung Wu

Per-Ake Larson

Justin Jon Levandoski

Rohit Madhukar Khandekar

Jonathan David Goldstein

Badrish Chandramouli

Abdul Hussain Quamar

Raul Castro Fernandez

Various technologies described herein pertain to accessing records of an in-memory database. A memory can include a primary storage and a data store can include a secondary storage. The primary storage includes a first subset of the records of the database, and the secondary storage includes a second subset of the records of the database. Moreover, the memory includes an update memo that includes timestamp notices. The timestamp notices specify statuses of records of the database having updates that span the primary storage and the secondary storage. The memory further includes a database management system that is executable by a processor. The database management system processes a transaction. Further, the database management system includes a storage interface component that accesses a record of the database for the transaction based on at least one of the timestamp notices of the update memo.

Transactional access to records on secondary storage in an in-memory database

High performance query processing and data analytics can be performed across architecturally diverse scales, such as single core, multi-core and/or multi-nodes. The high performance query processing and data analytics can include a separation of query computation, keying data, and data movement and parallel computation, thereby enhancing the capabilities of the query processing and data analytics, while allowing the specification of complex forms of data parallel computation that may execute across real-time and offline. The decoupling of data movement and parallel computation, as described herein can improve query processing and data analytics speed, can provide for the optimization of searches in a plurality of computing environments, and can provide the ability to search through a larger space of execution plans.

High performance query processing and data analytics

A system for clustering vertices in a streaming graph includes a structural sampler configured to receive a stream of edges. The structural sampler includes a reservoir manager configured to receive the stream of edges and create a structural reservoir and a support reservoir and a graph manager configured to receive the structural reservoir from the reservoir manager and to create a sampled graph from the structural reservoir, wherein the sampled graph includes one or more clusters that each include one or more connected vertices.

Clustering streaming graphs

Embodiments of the invention include methods for identifying one or more clusters in a streaming graph, the method includes receiving a stream of edges and sampling the stream of edges to create a structural reservoir and support reservoir. The method also includes creating a sampled graph from the structural reservoir and identifying the one or more clusters in the sampled graph by grouping one or more connected vertices in the sampled graph.

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Saint Paul, MN, United States of America

Ahmed Eldawy