Abstract: In one embodiment, a method comprises generating a backup of a table of a relational database by partitioning a plurality of records of the table among a plurality of Parquet files; and extracting metadata of the table into one or more metadata files; and requesting access of at least one of the plurality of Parquet files of the backup of the relational database responsive to an SQL query entered by a user into a user interface.

Abstract: A system and method for the device may include reading a plurality of rows of a database. In addition, the device may include generating a secondary hash value for each row of the plurality of rows. The device may include generating a plurality of row groups, each including a group of unique rows of the plurality of rows, based at least on the secondary hash value. Moreover, the device may include generating a primary hash value for each row group of the plurality of row groups. Also, the device may include updating a backup of the database based on a first-row group of the plurality of row groups, in response to determining that a primary hash value of the first-row group does not match any primary hash value associated with the backup of the database.

Abstract: A method and system for restoring a digital backup of a database application hosted on a virtual machine are presented. The method includes generating a bootable volume on a new virtual machine, wherein the new virtual machine is different than the virtual machine hosting the database application; importing data from a plurality of files into the bootable volume, wherein the plurality of files are included in a data backup of the digital backup, and wherein each file of the plurality of files is in a column-oriented storage format; and restoring a virtualization backup included in the digital backup into the bootable volume.

Abstract: A method and system for updating backup data for a database is presented. The method includes initiating a merging operation for a defined time interval; obtaining change data capture (CDC) data generated during the defined time interval; aggregating and ordering the CDC data by a key value; identifying backup data associated with the ordered CDC data; generating a candidate data object that represents data modified during the defined time interval; deduplicating the candidate data object by comparing a hash identifier of the candidate data object with a plurality of hash identifiers stored in a repository; storing the candidate data object when the comparison determines that no matching hash identifier exists in the repository; and updating backup metadata to associate the key value with the candidate data object.

Abstract: In one embodiment, a method comprises receiving a search request from a user; responsive to the search request, searching a plurality of metadata records corresponding to a plurality of databases, a metadata record of the plurality of metadata records comprising a plurality of searchable metadata items of a database of the plurality of databases, wherein the plurality of searchable metadata items of the metadata record includes a name of a table of the database; and providing search results responsive to the search request, the search results comprising indications of a subset of the plurality of metadata records that match the search request.

Abstract: A system and method for the device may include accessing a database application hosted on a virtual machine, the virtual machine deployed in a computing environment. In addition, the device may include exporting data stored in the database application to a plurality of files, each file utilizing a column-oriented storage format; generating a virtualization backup only of the virtual machine; and generating a data backup of the database application based on the plurality of files.

Abstract: Novel tools and techniques are provided for implementing detection and estimation of direct and reflected navigation satellite (e.g., global navigation satellite system (“GNSS”), etc.) signal parameters in a multipath environment. In various embodiments, logic of semiconductor package that is disposed on a user device concurrently receives a plurality of signals from a satellite(s), each signal travelling along a different path between each satellite(s) and the user device within a multipath environment. The logic identifies two or more signal peaks that fall within a tracking aperture based on analysis of the received signals, and determines peak parameter estimates for each signal peak based on measurements of signal parameters from at least one signal peak. The logic provides the determined peak parameter estimates for each signal peak to a position engine (“PE”) of the user device to calculate a navigation solution (e.g., position, velocity, and/or time, etc.) for the user device.

Abstract: Novel tools and techniques are provided for implementing detection and estimation of direct and reflected navigation satellite (e.g., global navigation satellite system (“GNSS”), etc.) signal parameters in a multipath environment. In various embodiments, logic of semiconductor package that is disposed on a user device concurrently receives a plurality of signals from a satellite(s), each signal travelling along a different path between each satellite(s) and the user device within a multipath environment. The logic identifies two or more signal peaks that fall within a tracking aperture based on analysis of the received signals, and determines peak parameter estimates for each signal peak based on measurements of signal parameters from at least one signal peak. The logic provides the determined peak parameter estimates for each signal peak to a position engine (“PE”) of the user device to calculate a navigation solution (e.g., position, velocity, and/or time, etc.) for the user device.

Abstract: Conventional 3D data models typically stored in a vertex buffer are processed so that all geometry is combined to one big geometry per node as a Vertex Buffer Object (VBO). The vertex contains position (x,y,z), Normal (x,y,z), but also an Object ID for each object. Further, a metadata database is created where all additional information is stored, and if there are multiple geometries in the same object, multiple Object IDs to the same metadata entry are added under primitives. By extracting metadata and Materials, it is made possible to handle a node as a single VBO.

Abstract: A reference file set having high-confidence malware severity classification is generated by selecting a subset of files from a group of files first observed during a recent observation period and including them in the subset. A plurality of other antivirus providers are polled for their third-party classification of the files in the subset and for their third-party classification of a plurality of files from the group of files not in the subset. A malware severity classification is determined for the files in the subset by aggregating the polled classifications from the other antivirus providers for the files in the subset after a stabilization period of time, and one or more files having a third-party classification from at least one of the polled other antivirus providers that changed during the stabilization period to the subset are added to the subset.

Abstract: Novel tools and techniques are provided for implementing detection and estimation of direct and reflected navigation satellite (e.g., global navigation satellite system (“GNSS”), etc.) signal parameters in a multipath environment. In various embodiments, logic of semiconductor package that is disposed on a user device concurrently receives a plurality of signals from a satellite(s), each signal travelling along a different path between each satellite(s) and the user device within a multipath environment. The logic identifies two or more signal peaks that fall within a tracking aperture based on analysis of the received signals, and determines peak parameter estimates for each signal peak based on measurements of signal parameters from at least one signal peak. The logic provides the determined peak parameter estimates for each signal peak to a position engine (“PE”) of the user device to calculate a navigation solution (e.g., position, velocity, and/or time, etc.) for the user device.

Abstract: Systems and methods for device type classification system include a rules engine and a machine learning engine. The machine learning engine can be trained using device type data from multiple networks. The machine learning engine and the rules engine can receive data for devices on a network at a first point in time. The data can be submitted to a rules engine and the machine learning engine, which each produce device type probabilities for devices on the network. The device type probabilities from the rules engine and the machine learning engine can be processed to determine device types for one or more devices on the network. As more data becomes available at later points in time, the additional data can be provided to the rules engine and the machine learning engine to update the device type determinations for the network.

Abstract: Described is a conventional 3D data models typically stored in a vertex buffer are processed so that all geometry is combined to one big geometry per node as an VBO. The vertex contains position (x,y,z), Normal (x,y,z), but also an Object ID for each object. Further, a metadata database is created where all additional information is stored, and if there are multiple geometries in the same object, multiple Object IDs to the same metadata entry are added under primitives. By extracting metadata and Materials, it is made possible to handle a node as a single Vertex Buffer Object (VBO).

Abstract: A method of identifying network devices includes transforming a first data set of feature-rich device characteristics of devices with known device identities to a second data set comprising feature-poor device characteristics with the known device identities. A third data set of feature-poor device characteristics of devices with known identities is collected. A statistical model is derived comprising one or more adjustments to the transformed data set, the statistical model reflecting a difference in statistical distribution between one or more characteristics of the second data set of transformed device characteristics and one or more corresponding and/or related characteristics of the third data set of feature-poor device characteristics. A device identification module is trained based on the second data set of feature-poor characteristics and the statistical model adjustments, the trained device identification module operable to use feature-poor device characteristics to identify network devices.

Abstract: A reference file set having high-confidence malware severity classification is generated by selecting a subset of files from a group of files first observed during a recent observation period and including them in the subset. A plurality of other antivirus providers are polled for their third-party classification of the files in the subset and for their third-party classification of a plurality of files from the group of files not in the subset. A malware severity classification is determined for the files in the subset by aggregating the polled classifications from the other antivirus providers for the files in the subset after a stabilization period of time, and one or more files having a third-party classification from at least one of the polled other antivirus providers that changed during the stabilization period to the subset are added to the subset.

Abstract: For a working wavelength in the range from 1 nm to 12 nm, a reflective optical element has, on a substrate, a multilayer system that includes at least two alternating materials having a different real part of the refractive index at the working wavelength. The multilayer system includes a first alternating material from the group formed from thorium, uranium, barium, nitrides thereof, carbides thereof, borides thereof, lanthanum carbide, lanthanum nitride, lanthanum boride, and a second alternating material from the group formed from carbon, boron, boron carbide, or lanthanum as first alternating material and carbon or boron as second alternating material. It has, on the side of the multilayer system remote from the substrate, a protective layer system including a nitride, an oxide and/or a platinum metal.

Abstract: Systems and methods for device type classification system include a rules engine and a machine learning engine. The machine learning engine can be trained using device type data from multiple networks. The machine learning engine and the rules engine can receive data for devices on a network at a first point in time. The data can be submitted to a rules engine and the machine learning engine, which each produce device type probabilities for devices on the network. The device type probabilities from the rules engine and the machine learning engine can be processed to determine device types for one or more devices on the network. As more data becomes available at later points in time, the additional data can be provided to the rules engine and the machine learning engine to update the device type determinations for the network.

Abstract: For a working wavelength in the range from 1 nm to 12 nm, a reflective optical element has, on a substrate, a multilayer system that includes at least two alternating materials having a different real part of the refractive index at the working wavelength. The multilayer system includes a first alternating material from the group formed from thorium, uranium, barium, nitrides thereof, carbides thereof, borides thereof, lanthanum carbide, lanthanum nitride, lanthanum boride, and a second alternating material from the group formed from carbon, boron, boron carbide, or lanthanum as first alternating material and carbon or boron as second alternating material. It has, on the side of the multilayer system remote from the substrate, a protective layer system including a nitride, an oxide and/or a platinum metal.

Abstract: Implementations provide for a system testing framework for computer systems. A method of the disclosure includes building representations of resources of a product to be tested, the resources built from source code of the product and revisions to the source code, initializing, on a single computing device, virtual resources from the representations, installing software of the product on the initialized virtual resources, saving a clean state of the initialized virtual resources and of the installed product on the initialized virtual resources, causing a test scenario to be performed on the installed software of the product executing on the virtual resources, and reverting the virtual resources and the installed software of the product to the clean state.

Abstract: Implementations provide for a system testing framework for computer systems. A method of the disclosure includes building representations of resources of a product to be tested, the resources built from source code of the product and revisions to the source code, initializing, on a single computing device, virtual resources from the representations, installing software of the product on the initialized virtual resources, saving a clean state of the initialized virtual resources and of the installed product on the initialized virtual resources, causing a test scenario to be performed on the installed software of the product executing on the virtual resources, and reverting the virtual resources and the installed software of the product to the clean state.