Oracle International Corporation

Anindya Chandra Patthak

Amit Ganesh

Vineet Marwah

Shasank Kisan Chavan

Jesse Kamp

Allison Holloway

Michael J Gleeson

Manosiz Bhattacharyya

Roger MacNicol

Lawrence J Ellison

Tirthankar Lahiri

Joydip Kundu

Chi-Kim Hoang

Dina Thomas

Gregory Michael Ferrar

Victor Chen

Kirk Meredith Edson

Kao Makino

Martin Reames

Subhradyuti Sarkar

Neelam Goyal

Qingchun Jiang

Rahul V Herwadkar

Allison L Holloway

Roger Macnicol

Martin A Reames

Disclosed is a system, method, and computer program product for implementing a log analytics method and system that can configure, collect, and analyze log records in an efficient manner. Machine learning-based classification can be performed to classify logs. This approach is used to group logs automatically using a machine learning infrastructure.

Method and system for implementing machine learning classifications

Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated 'in-place', that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language (“DDL”) commands that declare the row-major columns and column-major columns of a table.

Combined row and columnar storage for in-memory databases for OLTP and analytics workloads

Techniques for detecting an anomaly in queries of a relational database are disclosed. The techniques include identifying a set of attribute values from a query for accessing data within a database. Based on previously received queries, at least one of a joint probability for the set of attribute values or individual probabilities for the set of attribute values is determined. When at least one of the joint probability for the set of attribute values or an individual probability for one or more attribute values in the set of attribute values does not satisfy a probability cutoff, an indication that the query is anomalous is outputted.

Distribution-based analysis of queries for anomaly detection with adaptive thresholding

Techniques for detecting an anomaly in queries of a relational database are disclosed. The techniques include obtaining a set of semantic attributes for a query of a relational database. The semantic attributes are then used to calculate a set of similarities between the query and a set of non-anomalous queries of the relational database. When a highest similarity in the set of similarities does not meet a similarity threshold, an indication that the query is anomalous is outputted.

Attribute-based detection of anomalous relational database queries

A method, apparatus, and system for OZIP, a data compression and decompression codec, is provided. OZIP utilizes a fixed size static dictionary, which may be generated from a random sampling of input data to be compressed. Compression by direct token encoding to the static dictionary streamlines the encoding and avoids expensive conditional branching, facilitating hardware implementation and high parallelism. By bounding token definition sizes and static dictionary sizes to hardware architecture constraints such as word size or processor cache size, hardware implementation can be made fast and cost effective. For example, decompression may be accelerated by using SIMD instruction processor extensions. A highly granular block mapping in optional stored metadata allows compressed data to be accessed quickly at random, bypassing the processing overhead of dynamic dictionaries. Thus, OZIP can support low latency random data access for highly random workloads, such as for OLTP systems.

OZIP compression and decompression

Columns of a table are stored in either row-major format or column-major format in an in-memory DBMS. For a given table, one set of columns is stored in column-major format; another set of columns for a table are stored in row-major format. This way of storing columns of a table is referred to herein as dual-major format. In addition, a row in a dual-major table is updated 'in-place', that is, updates are made directly to column-major columns without creating an interim row-major form of the column-major columns of the row. Users may submit database definition language ('DDL') commands that declare the row-major columns and column-major columns of a table.

A method and apparatus for efficiently processing data in various formats in a single instruction multiple data ('SIMD') architecture is presented. Specifically, a method to unpack a fixed-width bit values in a bit stream to a fixed width byte stream in a SIMD architecture is presented. A method to unpack variable-length byte packed values in a byte stream in a SIMD architecture is presented. A method to decompress a run length encoded compressed bit-vector in a SIMD architecture is presented. A method to return the offset of each bit set to one in a bit-vector in a SIMD architecture is presented. A method to fetch bits from a bit-vector at specified offsets relative to a base in a SIMD architecture is presented. A method to compare values stored in two SIMD registers is presented.

Efficient hardware instructions for single instruction multiple data processors

Techniques are provided for more efficiently using the bandwidth of the I/O path between a CPU and volatile memory during the performance of database operation. Relational data from a relational table is stored in volatile memory as column vectors, where each column vector contains values for a particular column of the table. A binary-comparable format may be used to represent each value within a column vector, regardless of the data type associated with the column. The column vectors may be compressed and/or encoded while in volatile memory, and decompressed/decoded on-the-fly within the CPU. Alternatively, the CPU may be designed to perform operations directly on the compressed and/or encoded column vector data. In addition, techniques are described that enable the CPU to perform vector processing operations on the column vector values.

Techniques for maintaining column vectors of relational data within volatile memory

Loading values from a value vector into subregisters of a single instruction multiple data register

Efficient hardware instructions for processing bit vectors for single instruction multiple data processors

Techniques for more efficient usage of memory-to-CPU bandwidth

Growing community of inventors

San Jose, CA, United States of America

Anindya Chandra Patthak