Abstract: The disclosure features a method which includes inputting or receiving information on one or more features of a plurality of residential properties and prices of the residential properties including a marketed price, a listing price, and a closing price, providing the information to a Machine Learning Algorithm to determine the relationship between the one or more features and the prices of the residential properties to create a Machine Learned Model, inputting or receiving information on one or more features of a new residential property into the Machine Learned Model, and predicting a base price of the new residential property from the Machine Learned Model based on the one or more features of the new residential property. The disclosure also features one or more non-transitory, computer-readable storage media storing instructions capable of performing the method and a computer or computer system capable of performing the method.

Abstract: Present disclosure generally relates to data analytics in wireless networks, more particularly relates to systems and methods for optimizing supply demand in telecommunication network. System may prepare data for optimization using raw telecom data. Further, the system may build quadratic optimization objective function by reading index table (cell—grid information). System may build quadratic program inequality constraints, and prepare right hand side of constraints for all mentioned constraints maintaining the index. Thereafter, the system may execute optimizer and find the optimal solution ensuring hyper-parameter tuning, and calculate focal point of each cell using cell-grid allocation vector. The system may read the optimal solution from optimization process, and estimate electronic tilt values (i.e., Remote Electrical Tilt (RET)) ensuring the business guidelines. Thereafter, the system may use line of sight method to get inclination value (optimal tilt value) of cell from the focal point on the ground.

Abstract: Present disclosure generally relate to wireless networks, more particularly relates to systems and methods for determining spatial clusters in a network to enable connected community of telecommunication cellular towers. The system may prepare cell data using one or more circle data, city data, cell Identity (ID) data, latitude data, longitude data, azimuth data, and height data. System may compute geohash based on creating geohash neighbours and geohash bounding box data and compute sectors of the telecommunication towers. Further, the system may compute sector affinity of the telecommunication towers and perform clustering of the telecommunication towers.

Abstract: The present disclosure relates to a system and a method for processing distributed data files. The processor executes instructions to receive a set of instructions from a primary device, wherein the set of instructions comprises verification rules, validators, primary transformers and structure query transformers; generate processed data files by processing the distributed data files. The distributed data files are processed by performing at least one of: executing one of the verification rules, the validators and the primary transformers on the distributed data files; and transforming the distributed data files by executing the structure query transformers. The execution of the structured query transformers comprises steps of generating a dependency graph based upon dependencies between the structure query transformers; and determining a sequence of execution of the structured query transformers based upon the dependency graph; and transfer the processed data files to a data warehouse.

Abstract: Disclosed is a method and system for processing skewed datasets. The processor 202 is configured to capture a broadcast size of non-skewed datasets to be loaded onto a memory associated with one or more nodes in a distributed system. The skewed dataset is identified from two or more datasets to be joined. Each of the non-skewed dataset is divided into a plurality of non-skewed data chunks at the node and each of the non-skewed data chunk is broadcasted to one or more nodes having the skewed dataset. The joining operation is then performed between each of the skewed dataset and the non-skewed data chunk till all the non-skewed data chunks are consumed in the join operation. Resultant joined dataset is then collected as a single joined dataset from the nodes involved in the joining operation.

Abstract: The present disclosure relates to a system and a method for processing distributed data files. The processor executes instructions to receive a set of instructions from a primary device, wherein the set of instructions comprises verification rules, validators, primary transformers and structure query transformers; generate processed data files by processing the distributed data files. The distributed data files are processed by performing at least one of: executing one of the verification rules, the validators and the primary transformers on the distributed data files; and transforming the distributed data files by executing the structure query transformers. The execution of the structured query transformers comprises steps of generating a dependency graph based upon dependencies between the structure query transformers; and determining a sequence of execution of the structured query transformers based upon the dependency graph; and transfer the processed data files to a data warehouse.

Abstract: Disclosed is a method and system for joining datasets in a distributed computing environment. The system comprises a memory 206 and a processor 202. The processor 202 identifies a skewed dataset from two or more datasets to be joined. The processor 202 identifies a replication parameter from a configuration file. The processor 202 then assigns a randomly assigned machine number to each chunk of the skewed dataset owned by the nodes/machines involved in the join operation. The processor 202 forms copies of the non-skewed dataset equal to the replication parameter and adds the copy number to each sample of the copy of the non-skewed dataset formed. Further, the processor 202 merges each non-skewed dataset into the final copy of the non-skewed dataset, forming a single non skewed dataset. The processor 202 then repeats these steps for all the non-skewed datasets involved in the join operation resulting in generation of merged copies of all the non-skewed datasets and then performs the joining operation.

Abstract: Systems, methods and computer program products are described herein that can be used to help achieve a safe rollout of software in a production datacenter environment. In accordance with certain embodiments, cloud services requests from certain users of a cloud services system (e.g., users that are authorized to receive cloud services via computing devices running test versions of infrastructure software) are dynamically matched to clusters (groups of commonly-managed computing devices called nodes) that are capable of providing the requested services on nodes running test versions of infrastructure software. Within such clusters, the requested services are provided to the users on a subset of cluster nodes that run a test version of an infrastructure software component, while the remaining cluster nodes are not running the particular test version.

Abstract: Disclosed is a method and system for processing skewed datasets. The processor 202 is configured to capture a broadcast size of non-skewed datasets to be loaded onto a memory associated with one or more nodes in a distributed system. The skewed dataset is identified from two or more datasets to be joined. Each of the non-skewed dataset is divided into a plurality of non-skewed data chunks at the node and each of the non-skewed data chunk is broadcasted to one or more nodes having the skewed dataset. The joining operation is then performed between each of the skewed dataset and the non-skewed data chunk till all the non-skewed data chunks are consumed in the join operation. Resultant joined dataset is then collected as a single joined dataset from the nodes involved in the joining operation.

Abstract: Disclosed is a method and system for joining datasets in a distributed computing environment. The system comprises a memory 206 and a processor 202. The processor 202 identifies a skewed dataset from two or more datasets to be joined. The processor 202 identifies a replication parameter from a configuration file. The processor 202 then assigns a randomly assigned machine number to each chunk of the skewed dataset owned by the nodes/machines involved in the join operation. The processor 202 forms copies of the non-skewed dataset equal to the replication parameter and adds the copy number to each sample of the copy of the non-skewed dataset formed. Further, the processor 202 merges each non-skewed dataset into the final copy of the non-skewed dataset, forming a single non skewed dataset. The processor 202 then repeats these steps for all the non-skewed datasets involved in the join operation resulting in generation of merged copies of all the non-skewed datasets and then performs the joining operation.

Abstract: Disclosed method for generating synthetic data for minority classes in a very large dataset comprises grouping samples stored on several devices, into different groups. A pivot is identified to be used as a reference for grouping the samples into bins. The samples are assigned to a bin, based on a closest pivot. The samples are regrouped into different groups, based on identities of the bins, and each of the groups is distributed to the several devices. Samples belonging to majority class and minority classes for which synthetic data is not being generated are removed from each of the different groups. Samples of each of these groups are arranged in different M-Trees to facilitate identification of K-nearest neighbours for each sample within each of the different groups to generate K pairs of nearest neighbours. Finally, synthetic samples are generated for the K pairs of nearest neighbours by creating random samples.

Abstract: The present disclosure relates to a system and a method for processing distributed data files. The processor executes instructions to receive a set of instructions from a primary device, wherein the set of instructions comprises verification rules, validators, primary transformers and structure query transformers; generate processed data files by processing the distributed data files. The distributed data files are processed by performing at least one of: executing one of the verification rules, the validators and the primary transformers on the distributed data files; and transforming the distributed data files by executing the structure query transformers. The execution of the structured query transformers comprises steps of generating a dependency graph based upon dependencies between the structure query transformers; and determining a sequence of execution of the structured query transformers based upon the dependency graph; and transfer the processed data files to a data warehouse.

Abstract: Methods, systems, apparatuses, and computer program products are provided that enable the automated deployment of microservices to a network-accessible server set. The automated deployment may be based on constraint(s) that are specified by a declarative deployment model that is associated with the microservice to be deployed. For example, a centralized deployment orchestrator may receive microservice(s) and their associated declarative deployment model(s). The deployment orchestrator analyzes the declarative deployment model(s) and determines which microservice(s) are to be deployed based on the constraint(s) specified by the declarative deployment model(s). The foregoing techniques advantageously determine when to deploy microservice(s), while also minimizing human intervention typically required to deploy microservice(s).

Abstract: Disclosed method for generating synthetic data for minority classes in a very large dataset comprises grouping samples stored on several devices, into different groups. A pivot is identified to be used as a reference for grouping the samples into bins. The samples are assigned to a bin, based on a closest pivot. The samples are regrouped into different groups, based on identities of the bins, and each of the groups is distributed to the several devices. Samples belonging to majority class and minority classes for which synthetic data is not being generated are removed from each of the different groups. Samples of each of these groups are arranged in different M-Trees to facilitate identification of K-nearest neighbours for each sample within each of the different groups to generate K pairs of nearest neighbours. Finally, synthetic samples are generated for the K pairs of nearest neighbours by creating random samples.

Abstract: Software updates within one or more regions of a multi-tenant cloud are coordinated. Tenant vs. tenant conflicts, tenant vs. infrastructure provider conflicts, and conflicts between security and another priority are identified and resolved using a shared update coordinator, update priority specifications, and availability specifications. An infrastructure update request may be presented to tenants for approval. Postponed infrastructure updates may be prioritized higher. Preventing exploits of zero-day vulnerabilities may be prioritized over meeting availability targets. Updates may be merged to reduce downtime, even when the updates originate from independently controlled entities. Maximum downtime, minimum fault domains, minimum virtual machines, permitted update start times, and other availability criteria may be specified. Updates may be preempted, or allowed to complete, based on their relative priorities.

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Abstract: A process and apparatus for reducing the sulfur content of naphtha. The process includes introducing at least a portion of a naphtha feed stream to a selective hydrodesulfurization zone under selective hydrodesulfurization conditions in the presence of a selective hydrodesulfurization catalyst to form a low sulfur stream which contains mercaptan and thiophene compounds. At least a portion of the low sulfur stream is separated into at least two streams, a mercaptan rich stream containing mercaptan and thiophene compounds and an overhead stream containing hydrogen sulfide and liquid petroleum gas. The mercaptan rich stream is treated in an adsorbent zone to remove at least a portion of the mercaptan and thiophene compounds to form a mercaptan lean stream.

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.

Abstract: Processes for the production of transportation fuel from a renewable feedstock. A catalyst is used which is more selective to hydrodeoxygenate the fatty acid side chains compared to decarboxylation and decarbonylation reactions. A gaseous mixture of carbon monoxide and hydrogen can be supplied to the conversion zone. Water may also be introduced into the conversion zone to increase the amount of hydrogen.

Abstract: Methods, systems, and apparatuses manage rolling out of updates in a network-accessible server infrastructure which operates a plurality of instances of a supporting service. The supporting service is comprised by a plurality of service portions. The instances of the supporting service each include of the service portions. The instances of the supporting service are partitioned into a plurality of slices. Each instance is partitioned to include one or more of the slices, and each slice of an instance includes one or more of the service portions. A software update is deployed to the instances by applying the software update to the slices in a sequence such that the software update is applied to a same slice in parallel across the instances containing that same slice before being applied to a next slice, and waiting a wait time before applying the software domain to a next slice in the sequencing.